Evaluate Models
Author: Aaron S Coyner, PhD
Last update: 2021-09-27
Setup
library(tidyverse)
library(janitor)
library(tools)
library(caret)
library(ROCR)
library(caret)compute_auroc <- function(predictions, labels, title) {
pred <- prediction(predictions, labels)
auroc <- performance(pred, measure = 'auc')
auroc <- auroc@y.values[[1]]
perf <- performance(pred, 'tpr', 'fpr')
plot(perf,
main = title,
xlim=c(0,1),
ylim=c(0,1),
colorize = FALSE)
abline(a = 0,
b = 1,
col = 'red',
lty = 2)
text(0.8,
0.25,
paste('AUROC:', sprintf('%.3f', round(auroc, 3))))
}compute_aupr <- function(predictions, labels, title) {
n_black <- length(labels[labels == 1])
n_total <- length(labels)
pred <- prediction(predictions, labels)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
perf <- performance(pred, 'prec', 'rec')
plot(perf,
main = title,
xlim=c(0,1),
ylim=c(0,1),
colorize = FALSE)
abline(a = n_black/n_total,
b = 0,
col = 'red',
lty = 2)
text(0.2,
0.2,
paste('Null AUPR:', sprintf('%.3f', round(n_black / n_total, 3))))
text(0.8,
0.2,
paste('AUPR:', sprintf('%.3f', round(aupr, 3))))
}confusion_matrix <- function(df, column, threshold=0.5) {
preds <- as.factor(if_else(df[column] >= threshold, 1, 0))
confusionMatrix(preds, df$label, positive = '1')
}Load data
irop_data <- read_csv('/Volumes/External/irop_data/irop_07092020.csv') %>%
clean_names() %>%
distinct(subject_id, .keep_all = TRUE) %>%
select(subject_id, race)
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
.default = col_character(),
subjectVisitID = col_double(),
birthWeight = col_double(),
gestationalAgeWeeks = col_double(),
gestationalAgeDays = col_double(),
`PMA Weeks` = col_double(),
`PMA Days` = col_double(),
PMARaw = col_double(),
`Session Followup` = col_double(),
`Golden Reading Stage` = col_double(),
bloodTaken = col_double(),
salivaTaken = col_double()
)
ℹ Use `spec()` for the full column specifications.Warning: 5380 parsing failures.
row col expected actual file
1645 subjectVisitID a double NULL '/Volumes/External/irop_data/irop_07092020.csv'
1645 PMA Weeks a double NULL '/Volumes/External/irop_data/irop_07092020.csv'
1645 PMA Days a double NULL '/Volumes/External/irop_data/irop_07092020.csv'
1645 PMARaw a double NULL '/Volumes/External/irop_data/irop_07092020.csv'
1645 Session Followup a double NULL '/Volumes/External/irop_data/irop_07092020.csv'
.... ................ ........ ...... ...............................................
See problems(...) for more details.test_data <- read_csv('./out/datasets/test_data.csv') %>%
select(subject_id, image_id)
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
subject_id = col_character(),
race = col_character(),
variable = col_character(),
value = col_character(),
image_id = col_double(),
fundus_location = col_character(),
segmentation_location = col_character()
)image_level <- read_csv('./out/probabilities/retcam_filtered_0.csv', col_types = cols()) %>%
mutate(label = as.factor(if_else(str_detect(img_loc, 'black'), 1, 0)),
image_id = as.numeric(file_path_sans_ext(basename(img_loc)))) %>%
select(img_loc, image_id, label, retcam = probability) %>%
bind_cols(select(read_csv('./out/probabilities/retcam_filtered_0_random.csv', col_types = cols()), retcam_random = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_random.csv', col_types = cols()), filter_0_random = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0.csv', col_types = cols()), filter_0 = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_binarized.csv', col_types = cols()), filter_0_binarized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_skeletonized.csv', col_types = cols()), filter_0_skeletonized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_50.csv', col_types = cols()), filter_50 = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_50_binarized.csv', col_types = cols()), filter_50_binarized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_50_skeletonized.csv', col_types = cols()), filter_50_skeletonized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_100.csv', col_types = cols()), filter_100 = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_100_binarized.csv', col_types = cols()), filter_100_binarized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_100_skeletonized.csv', col_types = cols()), filter_100_skeletonized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_150.csv', col_types = cols()), filter_150 = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_150_binarized.csv', col_types = cols()), filter_150_binarized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_150_skeletonized.csv', col_types = cols()), filter_150_skeletonized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_200.csv', col_types = cols()), filter_200 = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_200_binarized.csv', col_types = cols()), filter_200_binarized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_200_skeletonized.csv', col_types = cols()), filter_200_skeletonized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_210.csv', col_types = cols()), filter_210 = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_210_binarized.csv', col_types = cols()), filter_210_binarized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_210_skeletonized.csv', col_types = cols()), filter_210_skeletonized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_220.csv', col_types = cols()), filter_220 = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_220_binarized.csv', col_types = cols()), filter_220_binarized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_220_skeletonized.csv', col_types = cols()), filter_220_skeletonized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_230.csv', col_types = cols()), filter_230 = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_230_binarized.csv', col_types = cols()), filter_230_binarized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_230_skeletonized.csv', col_types = cols()), filter_230_skeletonized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_240.csv', col_types = cols()), filter_240 = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_240_binarized.csv', col_types = cols()), filter_240_binarized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_240_skeletonized.csv', col_types = cols()), filter_240_skeletonized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_250.csv', col_types = cols()), filter_250 = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_250_binarized.csv', col_types = cols()), filter_250_binarized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_250_skeletonized.csv', col_types = cols()), filter_250_skeletonized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_257.csv', col_types = cols()), filter_257 = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_257_binarized.csv', col_types = cols()), filter_257_binarized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_257_skeletonized.csv', col_types = cols()), filter_257_skeletonized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_10.csv', col_types = cols()), filter_10 = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_10_binarized.csv', col_types = cols()), filter_10_binarized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_10_skeletonized.csv', col_types = cols()), filter_10_skeletonized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_75_150.csv', col_types = cols()), filter_75 = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_75_150_binarized.csv', col_types = cols()), filter_75_binarized = probability)) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_75_150_skeletonized.csv', col_types = cols()), filter_75_skeletonized = probability)) %>%
inner_join(test_data, by = 'image_id') %>%
select(subject_id, everything(), -img_loc)
subject_level <- image_level %>%
group_by(subject_id) %>%
mutate(across(c(-image_id, -label), median)) %>%
ungroup() %>%
distinct(subject_id, .keep_all = TRUE)Image-level Analysis
RetCam Images
compute_aupr(image_level$retcam, image_level$label, 'PR: Raw RetCam Images')
compute_auroc(image_level$retcam, image_level$label, 'ROC: Raw RetCam Images')
confusion_matrix(image_level, 'retcam')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 819 10
1 8 513
Accuracy : 0.9867
95% CI : (0.979, 0.9921)
No Information Rate : 0.6126
P-Value [Acc > NIR] : <2e-16
Kappa : 0.9719
Mcnemar's Test P-Value : 0.8137
Sensitivity : 0.9809
Specificity : 0.9903
Pos Pred Value : 0.9846
Neg Pred Value : 0.9879
Prevalence : 0.3874
Detection Rate : 0.3800
Detection Prevalence : 0.3859
Balanced Accuracy : 0.9856
'Positive' Class : 1
compute_aupr(image_level$retcam_random, image_level$label, 'PR: Raw RetCam Images - Shuffled Labels')
compute_auroc(image_level$retcam_random, image_level$label, 'ROC: Raw RetCam Images - Shuffled Labels')
confusion_matrix(image_level, 'retcam_random')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 444 113
1 383 410
Accuracy : 0.6326
95% CI : (0.6062, 0.6584)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 0.06902
Kappa : 0.293
Mcnemar's Test P-Value : < 2e-16
Sensitivity : 0.7839
Specificity : 0.5369
Pos Pred Value : 0.5170
Neg Pred Value : 0.7971
Prevalence : 0.3874
Detection Rate : 0.3037
Detection Prevalence : 0.5874
Balanced Accuracy : 0.6604
'Positive' Class : 1
Segmentations: zero all pixels < 0
compute_aupr(image_level$filter_0, image_level$label, 'PR: Segmentations: zero all pixels < 0')
compute_auroc(image_level$filter_0, image_level$label, 'ROC: Segmentations: zero all pixels < 0')
confusion_matrix(image_level, 'filter_0')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 758 74
1 69 449
Accuracy : 0.8941
95% CI : (0.8764, 0.91)
No Information Rate : 0.6126
P-Value [Acc > NIR] : <2e-16
Kappa : 0.7764
Mcnemar's Test P-Value : 0.738
Sensitivity : 0.8585
Specificity : 0.9166
Pos Pred Value : 0.8668
Neg Pred Value : 0.9111
Prevalence : 0.3874
Detection Rate : 0.3326
Detection Prevalence : 0.3837
Balanced Accuracy : 0.8875
'Positive' Class : 1
compute_aupr(image_level$filter_0_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 0 and binarize')
compute_auroc(image_level$filter_0_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 0 and binarize')
confusion_matrix(image_level, 'filter_0_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 733 31
1 94 492
Accuracy : 0.9074
95% CI : (0.8907, 0.9223)
No Information Rate : 0.6126
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.8091
Mcnemar's Test P-Value : 2.932e-08
Sensitivity : 0.9407
Specificity : 0.8863
Pos Pred Value : 0.8396
Neg Pred Value : 0.9594
Prevalence : 0.3874
Detection Rate : 0.3644
Detection Prevalence : 0.4341
Balanced Accuracy : 0.9135
'Positive' Class : 1
compute_aupr(image_level$filter_0_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 0 and skeletonize')
compute_auroc(image_level$filter_0_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 0 and skeletonize')
confusion_matrix(image_level, 'filter_0_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 691 38
1 136 485
Accuracy : 0.8711
95% CI : (0.8521, 0.8885)
No Information Rate : 0.6126
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.7375
Mcnemar's Test P-Value : 1.93e-13
Sensitivity : 0.9273
Specificity : 0.8356
Pos Pred Value : 0.7810
Neg Pred Value : 0.9479
Prevalence : 0.3874
Detection Rate : 0.3593
Detection Prevalence : 0.4600
Balanced Accuracy : 0.8814
'Positive' Class : 1
compute_aupr(image_level$filter_0_random, image_level$label, 'PR: Segmentations: zero all pixels < 0 - Shuffled Labels')
compute_auroc(image_level$filter_0_random, image_level$label, 'ROC: Segmentations: zero all pixels < 0 - Shuffled Labels')
confusion_matrix(image_level, 'filter_0_random')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 703 418
1 124 105
Accuracy : 0.5985
95% CI : (0.5718, 0.6248)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 0.8619
Kappa : 0.0567
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.20076
Specificity : 0.85006
Pos Pred Value : 0.45852
Neg Pred Value : 0.62712
Prevalence : 0.38741
Detection Rate : 0.07778
Detection Prevalence : 0.16963
Balanced Accuracy : 0.52541
'Positive' Class : 1
Segmentations: zero all pixels < 50
compute_aupr(image_level$filter_50, image_level$label, 'PR: Segmentations: zero all pixels < 50')
compute_auroc(image_level$filter_50, image_level$label, 'ROC: Segmentations: zero all pixels < 50')
confusion_matrix(image_level, 'filter_50')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 588 63
1 239 460
Accuracy : 0.7763
95% CI : (0.7531, 0.7983)
No Information Rate : 0.6126
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.5561
Mcnemar's Test P-Value : < 2.2e-16
Sensitivity : 0.8795
Specificity : 0.7110
Pos Pred Value : 0.6581
Neg Pred Value : 0.9032
Prevalence : 0.3874
Detection Rate : 0.3407
Detection Prevalence : 0.5178
Balanced Accuracy : 0.7953
'Positive' Class : 1
compute_aupr(image_level$filter_50_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 50 and binarize')
compute_auroc(image_level$filter_50_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 50 and binarize')
confusion_matrix(image_level, 'filter_0_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 733 31
1 94 492
Accuracy : 0.9074
95% CI : (0.8907, 0.9223)
No Information Rate : 0.6126
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.8091
Mcnemar's Test P-Value : 2.932e-08
Sensitivity : 0.9407
Specificity : 0.8863
Pos Pred Value : 0.8396
Neg Pred Value : 0.9594
Prevalence : 0.3874
Detection Rate : 0.3644
Detection Prevalence : 0.4341
Balanced Accuracy : 0.9135
'Positive' Class : 1
compute_aupr(image_level$filter_50_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 50 and skeletonize')
compute_auroc(image_level$filter_50_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 50 and skeletonize')
confusion_matrix(image_level, 'filter_50_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 408 20
1 419 503
Accuracy : 0.6748
95% CI : (0.6491, 0.6998)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 1.202e-06
Kappa : 0.3991
Mcnemar's Test P-Value : < 2.2e-16
Sensitivity : 0.9618
Specificity : 0.4933
Pos Pred Value : 0.5456
Neg Pred Value : 0.9533
Prevalence : 0.3874
Detection Rate : 0.3726
Detection Prevalence : 0.6830
Balanced Accuracy : 0.7276
'Positive' Class : 1
Segmentations: zero all pixels < 100
compute_aupr(image_level$filter_100, image_level$label, 'PR: Segmentations: zero all pixels < 100')
compute_auroc(image_level$filter_100, image_level$label, 'ROC: Segmentations: zero all pixels < 100')
confusion_matrix(image_level, 'filter_100')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 736 210
1 91 313
Accuracy : 0.777
95% CI : (0.7539, 0.799)
No Information Rate : 0.6126
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.5098
Mcnemar's Test P-Value : 1.036e-11
Sensitivity : 0.5985
Specificity : 0.8900
Pos Pred Value : 0.7748
Neg Pred Value : 0.7780
Prevalence : 0.3874
Detection Rate : 0.2319
Detection Prevalence : 0.2993
Balanced Accuracy : 0.7442
'Positive' Class : 1
compute_aupr(image_level$filter_100_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 100 and binarize')
compute_auroc(image_level$filter_100_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 100 and binarize')
confusion_matrix(image_level, 'filter_100_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 669 142
1 158 381
Accuracy : 0.7778
95% CI : (0.7546, 0.7997)
No Information Rate : 0.6126
P-Value [Acc > NIR] : <2e-16
Kappa : 0.5344
Mcnemar's Test P-Value : 0.3865
Sensitivity : 0.7285
Specificity : 0.8089
Pos Pred Value : 0.7069
Neg Pred Value : 0.8249
Prevalence : 0.3874
Detection Rate : 0.2822
Detection Prevalence : 0.3993
Balanced Accuracy : 0.7687
'Positive' Class : 1
compute_aupr(image_level$filter_100_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 100 and skeletonize')
compute_auroc(image_level$filter_100_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 100 and skeletonize')
confusion_matrix(image_level, 'filter_100_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 632 144
1 195 379
Accuracy : 0.7489
95% CI : (0.7249, 0.7718)
No Information Rate : 0.6126
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.4803
Mcnemar's Test P-Value : 0.006615
Sensitivity : 0.7247
Specificity : 0.7642
Pos Pred Value : 0.6603
Neg Pred Value : 0.8144
Prevalence : 0.3874
Detection Rate : 0.2807
Detection Prevalence : 0.4252
Balanced Accuracy : 0.7444
'Positive' Class : 1
Segmentations: zero all pixels < 150
compute_aupr(image_level$filter_150, image_level$label, 'PR: Segmentations: zero all pixels < 150')
compute_auroc(image_level$filter_150, image_level$label, 'ROC: Segmentations: zero all pixels < 150')
confusion_matrix(image_level, 'filter_150')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 625 118
1 202 405
Accuracy : 0.763
95% CI : (0.7393, 0.7854)
No Information Rate : 0.6126
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.5149
Mcnemar's Test P-Value : 3.487e-06
Sensitivity : 0.7744
Specificity : 0.7557
Pos Pred Value : 0.6672
Neg Pred Value : 0.8412
Prevalence : 0.3874
Detection Rate : 0.3000
Detection Prevalence : 0.4496
Balanced Accuracy : 0.7651
'Positive' Class : 1
compute_aupr(image_level$filter_150_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 150 and binarize')
compute_auroc(image_level$filter_150_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 150 and binarize')
confusion_matrix(image_level, 'filter_150_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 702 167
1 125 356
Accuracy : 0.7837
95% CI : (0.7608, 0.8054)
No Information Rate : 0.6126
P-Value [Acc > NIR] : < 2e-16
Kappa : 0.5375
Mcnemar's Test P-Value : 0.01642
Sensitivity : 0.6807
Specificity : 0.8489
Pos Pred Value : 0.7401
Neg Pred Value : 0.8078
Prevalence : 0.3874
Detection Rate : 0.2637
Detection Prevalence : 0.3563
Balanced Accuracy : 0.7648
'Positive' Class : 1
compute_aupr(image_level$filter_150_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 150 and skeletonize')
compute_auroc(image_level$filter_150_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 150 and skeletonize')
confusion_matrix(image_level, 'filter_150_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 641 162
1 186 361
Accuracy : 0.7422
95% CI : (0.718, 0.7654)
No Information Rate : 0.6126
P-Value [Acc > NIR] : <2e-16
Kappa : 0.4614
Mcnemar's Test P-Value : 0.2176
Sensitivity : 0.6902
Specificity : 0.7751
Pos Pred Value : 0.6600
Neg Pred Value : 0.7983
Prevalence : 0.3874
Detection Rate : 0.2674
Detection Prevalence : 0.4052
Balanced Accuracy : 0.7327
'Positive' Class : 1
Segmentations: zero all pixels < 200
compute_aupr(image_level$filter_200, image_level$label, 'PR: Segmentations: zero all pixels < 200')
compute_auroc(image_level$filter_200, image_level$label, 'ROC: Segmentations: zero all pixels < 200')
confusion_matrix(image_level, 'filter_200')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 548 121
1 279 402
Accuracy : 0.7037
95% CI : (0.6786, 0.728)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 1.64e-12
Kappa : 0.4086
Mcnemar's Test P-Value : 4.16e-15
Sensitivity : 0.7686
Specificity : 0.6626
Pos Pred Value : 0.5903
Neg Pred Value : 0.8191
Prevalence : 0.3874
Detection Rate : 0.2978
Detection Prevalence : 0.5044
Balanced Accuracy : 0.7156
'Positive' Class : 1
compute_aupr(image_level$filter_200_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 200 and binarize')
compute_auroc(image_level$filter_200_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 200 and binarize')
confusion_matrix(image_level, 'filter_200_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 661 233
1 166 290
Accuracy : 0.7044
95% CI : (0.6793, 0.7287)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 1.083e-12
Kappa : 0.3623
Mcnemar's Test P-Value : 0.0009527
Sensitivity : 0.5545
Specificity : 0.7993
Pos Pred Value : 0.6360
Neg Pred Value : 0.7394
Prevalence : 0.3874
Detection Rate : 0.2148
Detection Prevalence : 0.3378
Balanced Accuracy : 0.6769
'Positive' Class : 1
compute_aupr(image_level$filter_200_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 200 and skeletonize')
compute_auroc(image_level$filter_200_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 200 and skeletonize')
confusion_matrix(image_level, 'filter_200_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 766 365
1 61 158
Accuracy : 0.6844
95% CI : (0.6589, 0.7092)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 2.354e-08
Kappa : 0.2557
Mcnemar's Test P-Value : < 2.2e-16
Sensitivity : 0.3021
Specificity : 0.9262
Pos Pred Value : 0.7215
Neg Pred Value : 0.6773
Prevalence : 0.3874
Detection Rate : 0.1170
Detection Prevalence : 0.1622
Balanced Accuracy : 0.6142
'Positive' Class : 1
Segmentations: zero all pixels < 210
compute_aupr(image_level$filter_210, image_level$label, 'PR: Segmentations: zero all pixels < 210')
compute_auroc(image_level$filter_210, image_level$label, 'ROC: Segmentations: zero all pixels < 210')
confusion_matrix(image_level, 'filter_210')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 712 326
1 115 197
Accuracy : 0.6733
95% CI : (0.6476, 0.6983)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 2.096e-06
Kappa : 0.2566
Mcnemar's Test P-Value : < 2.2e-16
Sensitivity : 0.3767
Specificity : 0.8609
Pos Pred Value : 0.6314
Neg Pred Value : 0.6859
Prevalence : 0.3874
Detection Rate : 0.1459
Detection Prevalence : 0.2311
Balanced Accuracy : 0.6188
'Positive' Class : 1
compute_aupr(image_level$filter_210_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 210 and binarize')
compute_auroc(image_level$filter_210_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 210 and binarize')
confusion_matrix(image_level, 'filter_210_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 639 203
1 188 320
Accuracy : 0.7104
95% CI : (0.6854, 0.7345)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 3.463e-14
Kappa : 0.3866
Mcnemar's Test P-Value : 0.4789
Sensitivity : 0.6119
Specificity : 0.7727
Pos Pred Value : 0.6299
Neg Pred Value : 0.7589
Prevalence : 0.3874
Detection Rate : 0.2370
Detection Prevalence : 0.3763
Balanced Accuracy : 0.6923
'Positive' Class : 1
compute_aupr(image_level$filter_210_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 210 and skeletonize')
compute_auroc(image_level$filter_210_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 210 and skeletonize')
confusion_matrix(image_level, 'filter_210_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 562 160
1 265 363
Accuracy : 0.6852
95% CI : (0.6597, 0.7099)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 1.699e-08
Kappa : 0.3603
Mcnemar's Test P-Value : 4.541e-07
Sensitivity : 0.6941
Specificity : 0.6796
Pos Pred Value : 0.5780
Neg Pred Value : 0.7784
Prevalence : 0.3874
Detection Rate : 0.2689
Detection Prevalence : 0.4652
Balanced Accuracy : 0.6868
'Positive' Class : 1
Segmentations: zero all pixels < 220
compute_aupr(image_level$filter_220, image_level$label, 'PR: Segmentations: zero all pixels < 220')
compute_auroc(image_level$filter_220, image_level$label, 'ROC: Segmentations: zero all pixels < 220')
confusion_matrix(image_level, 'filter_220')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 576 224
1 251 299
Accuracy : 0.6481
95% CI : (0.622, 0.6736)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 0.003819
Kappa : 0.2657
Mcnemar's Test P-Value : 0.232884
Sensitivity : 0.5717
Specificity : 0.6965
Pos Pred Value : 0.5436
Neg Pred Value : 0.7200
Prevalence : 0.3874
Detection Rate : 0.2215
Detection Prevalence : 0.4074
Balanced Accuracy : 0.6341
'Positive' Class : 1
compute_aupr(image_level$filter_220_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 220 and binarize')
compute_auroc(image_level$filter_220_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 220 and binarize')
confusion_matrix(image_level, 'filter_220_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 545 162
1 282 361
Accuracy : 0.6711
95% CI : (0.6453, 0.6961)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 4.710e-06
Kappa : 0.3351
Mcnemar's Test P-Value : 1.628e-08
Sensitivity : 0.6902
Specificity : 0.6590
Pos Pred Value : 0.5614
Neg Pred Value : 0.7709
Prevalence : 0.3874
Detection Rate : 0.2674
Detection Prevalence : 0.4763
Balanced Accuracy : 0.6746
'Positive' Class : 1
compute_aupr(image_level$filter_220_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 220 and skeletonize')
compute_auroc(image_level$filter_220_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 220 and skeletonize')
confusion_matrix(image_level, 'filter_220_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 800 450
1 27 73
Accuracy : 0.6467
95% CI : (0.6205, 0.6722)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 0.005317
Kappa : 0.1256
Mcnemar's Test P-Value : < 2.2e-16
Sensitivity : 0.13958
Specificity : 0.96735
Pos Pred Value : 0.73000
Neg Pred Value : 0.64000
Prevalence : 0.38741
Detection Rate : 0.05407
Detection Prevalence : 0.07407
Balanced Accuracy : 0.55347
'Positive' Class : 1
Segmentations: zero all pixels < 230
compute_aupr(image_level$filter_230, image_level$label, 'PR: Segmentations: zero all pixels < 230')
compute_auroc(image_level$filter_230, image_level$label, 'ROC: Segmentations: zero all pixels < 230')
confusion_matrix(image_level, 'filter_230')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 464 160
1 363 363
Accuracy : 0.6126
95% CI : (0.586, 0.6387)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 0.512
Kappa : 0.2381
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.6941
Specificity : 0.5611
Pos Pred Value : 0.5000
Neg Pred Value : 0.7436
Prevalence : 0.3874
Detection Rate : 0.2689
Detection Prevalence : 0.5378
Balanced Accuracy : 0.6276
'Positive' Class : 1
compute_aupr(image_level$filter_230_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 230 and binarize')
compute_auroc(image_level$filter_230_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 230 and binarize')
confusion_matrix(image_level, 'filter_230_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 246 46
1 581 477
Accuracy : 0.5356
95% CI : (0.5085, 0.5624)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 1
Kappa : 0.1764
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.9120
Specificity : 0.2975
Pos Pred Value : 0.4509
Neg Pred Value : 0.8425
Prevalence : 0.3874
Detection Rate : 0.3533
Detection Prevalence : 0.7837
Balanced Accuracy : 0.6048
'Positive' Class : 1
compute_aupr(image_level$filter_230_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 230 and skeletonize')
compute_auroc(image_level$filter_230_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 230 and skeletonize')
confusion_matrix(image_level, 'filter_230_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 365 110
1 462 413
Accuracy : 0.5763
95% CI : (0.5494, 0.6028)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 0.997
Kappa : 0.2056
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.7897
Specificity : 0.4414
Pos Pred Value : 0.4720
Neg Pred Value : 0.7684
Prevalence : 0.3874
Detection Rate : 0.3059
Detection Prevalence : 0.6481
Balanced Accuracy : 0.6155
'Positive' Class : 1
Segmentations: zero all pixels < 240
compute_aupr(image_level$filter_240, image_level$label, 'PR: Segmentations: zero all pixels < 240')
compute_auroc(image_level$filter_240, image_level$label, 'ROC: Segmentations: zero all pixels < 240')
confusion_matrix(image_level, 'filter_240')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 414 183
1 413 340
Accuracy : 0.5585
95% CI : (0.5316, 0.5852)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 1
Kappa : 0.1394
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.6501
Specificity : 0.5006
Pos Pred Value : 0.4515
Neg Pred Value : 0.6935
Prevalence : 0.3874
Detection Rate : 0.2519
Detection Prevalence : 0.5578
Balanced Accuracy : 0.5754
'Positive' Class : 1
compute_aupr(image_level$filter_240_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 240 and binarize')
compute_auroc(image_level$filter_240_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 240 and binarize')
confusion_matrix(image_level, 'filter_240_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 258 83
1 569 440
Accuracy : 0.517
95% CI : (0.49, 0.544)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 1
Kappa : 0.1309
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.8413
Specificity : 0.3120
Pos Pred Value : 0.4361
Neg Pred Value : 0.7566
Prevalence : 0.3874
Detection Rate : 0.3259
Detection Prevalence : 0.7474
Balanced Accuracy : 0.5766
'Positive' Class : 1
compute_aupr(image_level$filter_240_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 240 and skeletonize')
compute_auroc(image_level$filter_240_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 240 and skeletonize')
confusion_matrix(image_level, 'filter_240_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 279 103
1 548 420
Accuracy : 0.5178
95% CI : (0.4907, 0.5447)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 1
Kappa : 0.1214
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.8031
Specificity : 0.3374
Pos Pred Value : 0.4339
Neg Pred Value : 0.7304
Prevalence : 0.3874
Detection Rate : 0.3111
Detection Prevalence : 0.7170
Balanced Accuracy : 0.5702
'Positive' Class : 1
Segmentations: zero all pixels < 250
compute_aupr(image_level$filter_250, image_level$label, 'PR: Segmentations: zero all pixels < 250')
compute_auroc(image_level$filter_250, image_level$label, 'ROC: Segmentations: zero all pixels < 250')
confusion_matrix(image_level, 'filter_250')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 51 9
1 776 514
Accuracy : 0.4185
95% CI : (0.392, 0.4454)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 1
Kappa : 0.035
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.98279
Specificity : 0.06167
Pos Pred Value : 0.39845
Neg Pred Value : 0.85000
Prevalence : 0.38741
Detection Rate : 0.38074
Detection Prevalence : 0.95556
Balanced Accuracy : 0.52223
'Positive' Class : 1
compute_aupr(image_level$filter_250_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 250 and binarize')
compute_auroc(image_level$filter_250_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 250 and binarize')
confusion_matrix(image_level, 'filter_250_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 50 8
1 777 515
Accuracy : 0.4185
95% CI : (0.392, 0.4454)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 1
Kappa : 0.0356
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.98470
Specificity : 0.06046
Pos Pred Value : 0.39861
Neg Pred Value : 0.86207
Prevalence : 0.38741
Detection Rate : 0.38148
Detection Prevalence : 0.95704
Balanced Accuracy : 0.52258
'Positive' Class : 1
compute_aupr(image_level$filter_250_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 250 and skeletonize')
compute_auroc(image_level$filter_250_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 250 and skeletonize')
confusion_matrix(image_level, 'filter_250_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 53 9
1 774 514
Accuracy : 0.42
95% CI : (0.3935, 0.4468)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 1
Kappa : 0.0369
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.98279
Specificity : 0.06409
Pos Pred Value : 0.39907
Neg Pred Value : 0.85484
Prevalence : 0.38741
Detection Rate : 0.38074
Detection Prevalence : 0.95407
Balanced Accuracy : 0.52344
'Positive' Class : 1
Segmentations: zero all pixels < 257
compute_aupr(image_level$filter_257, image_level$label, 'PR: Segmentations: zero all pixels < 257')
compute_auroc(image_level$filter_257, image_level$label, 'ROC: Segmentations: zero all pixels < 257')
confusion_matrix(image_level, 'filter_257')Warning in confusionMatrix.default(preds, df$label, positive = "1") :
Levels are not in the same order for reference and data. Refactoring data to match.Confusion Matrix and Statistics
Reference
Prediction 0 1
0 827 523
1 0 0
Accuracy : 0.6126
95% CI : (0.586, 0.6387)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 0.512
Kappa : 0
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.0000
Specificity : 1.0000
Pos Pred Value : NaN
Neg Pred Value : 0.6126
Prevalence : 0.3874
Detection Rate : 0.0000
Detection Prevalence : 0.0000
Balanced Accuracy : 0.5000
'Positive' Class : 1
compute_aupr(image_level$filter_257_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 257 and binarize')
compute_auroc(image_level$filter_257_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 257 and binarize')
confusion_matrix(image_level, 'filter_257_binarized')Warning in confusionMatrix.default(preds, df$label, positive = "1") :
Levels are not in the same order for reference and data. Refactoring data to match.Confusion Matrix and Statistics
Reference
Prediction 0 1
0 827 523
1 0 0
Accuracy : 0.6126
95% CI : (0.586, 0.6387)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 0.512
Kappa : 0
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.0000
Specificity : 1.0000
Pos Pred Value : NaN
Neg Pred Value : 0.6126
Prevalence : 0.3874
Detection Rate : 0.0000
Detection Prevalence : 0.0000
Balanced Accuracy : 0.5000
'Positive' Class : 1
compute_aupr(image_level$filter_257_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 257 and skeletonize')
compute_auroc(image_level$filter_257_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 257 and skeletonize')
confusion_matrix(image_level, 'filter_257_skeletonized')Warning in confusionMatrix.default(preds, df$label, positive = "1") :
Levels are not in the same order for reference and data. Refactoring data to match.Confusion Matrix and Statistics
Reference
Prediction 0 1
0 827 523
1 0 0
Accuracy : 0.6126
95% CI : (0.586, 0.6387)
No Information Rate : 0.6126
P-Value [Acc > NIR] : 0.512
Kappa : 0
Mcnemar's Test P-Value : <2e-16
Sensitivity : 0.0000
Specificity : 1.0000
Pos Pred Value : NaN
Neg Pred Value : 0.6126
Prevalence : 0.3874
Detection Rate : 0.0000
Detection Prevalence : 0.0000
Balanced Accuracy : 0.5000
'Positive' Class : 1
Segmentations: zero all pixels > 10
compute_aupr(image_level$filter_10, image_level$label, 'PR: Segmentations: zero all pixels < 10')
compute_auroc(image_level$filter_10, image_level$label, 'ROC: Segmentations: zero all pixels < 10')
confusion_matrix(image_level, 'filter_10')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 763 196
1 64 327
Accuracy : 0.8074
95% CI : (0.7853, 0.8281)
No Information Rate : 0.6126
P-Value [Acc > NIR] : < 2e-16
Kappa : 0.5745
Mcnemar's Test P-Value : 4.5e-16
Sensitivity : 0.6252
Specificity : 0.9226
Pos Pred Value : 0.8363
Neg Pred Value : 0.7956
Prevalence : 0.3874
Detection Rate : 0.2422
Detection Prevalence : 0.2896
Balanced Accuracy : 0.7739
'Positive' Class : 1
compute_aupr(image_level$filter_10_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 10 and binarize')
compute_auroc(image_level$filter_10_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 10 and binarize')
confusion_matrix(image_level, 'filter_10_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 621 7
1 206 516
Accuracy : 0.8422
95% CI : (0.8217, 0.8613)
No Information Rate : 0.6126
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.6893
Mcnemar's Test P-Value : < 2.2e-16
Sensitivity : 0.9866
Specificity : 0.7509
Pos Pred Value : 0.7147
Neg Pred Value : 0.9889
Prevalence : 0.3874
Detection Rate : 0.3822
Detection Prevalence : 0.5348
Balanced Accuracy : 0.8688
'Positive' Class : 1
compute_aupr(image_level$filter_10_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 10 and skeletonize')
compute_auroc(image_level$filter_10_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 10 and skeletonize')
confusion_matrix(image_level, 'filter_10_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 696 43
1 131 480
Accuracy : 0.8711
95% CI : (0.8521, 0.8885)
No Information Rate : 0.6126
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.7366
Mcnemar's Test P-Value : 4.24e-11
Sensitivity : 0.9178
Specificity : 0.8416
Pos Pred Value : 0.7856
Neg Pred Value : 0.9418
Prevalence : 0.3874
Detection Rate : 0.3556
Detection Prevalence : 0.4526
Balanced Accuracy : 0.8797
'Positive' Class : 1
Segmentations: zero all pixels < 75 and > 150
compute_aupr(image_level$filter_75, image_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150')
compute_auroc(image_level$filter_75, image_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150')
confusion_matrix(image_level, 'filter_75')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 597 77
1 230 446
Accuracy : 0.7726
95% CI : (0.7493, 0.7947)
No Information Rate : 0.6126
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.5453
Mcnemar's Test P-Value : < 2.2e-16
Sensitivity : 0.8528
Specificity : 0.7219
Pos Pred Value : 0.6598
Neg Pred Value : 0.8858
Prevalence : 0.3874
Detection Rate : 0.3304
Detection Prevalence : 0.5007
Balanced Accuracy : 0.7873
'Positive' Class : 1
compute_aupr(image_level$filter_75_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and binarize')
compute_auroc(image_level$filter_75_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and binarize')
confusion_matrix(image_level, 'filter_75_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 545 69
1 282 454
Accuracy : 0.74
95% CI : (0.7157, 0.7632)
No Information Rate : 0.6126
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.4904
Mcnemar's Test P-Value : < 2.2e-16
Sensitivity : 0.8681
Specificity : 0.6590
Pos Pred Value : 0.6168
Neg Pred Value : 0.8876
Prevalence : 0.3874
Detection Rate : 0.3363
Detection Prevalence : 0.5452
Balanced Accuracy : 0.7635
'Positive' Class : 1
compute_aupr(image_level$filter_75_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and skeletonize')
compute_auroc(image_level$filter_75_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and skeletonize')
confusion_matrix(image_level, 'filter_75_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 726 224
1 101 299
Accuracy : 0.7593
95% CI : (0.7355, 0.7819)
No Information Rate : 0.6126
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.4699
Mcnemar's Test P-Value : 1.312e-11
Sensitivity : 0.5717
Specificity : 0.8779
Pos Pred Value : 0.7475
Neg Pred Value : 0.7642
Prevalence : 0.3874
Detection Rate : 0.2215
Detection Prevalence : 0.2963
Balanced Accuracy : 0.7248
'Positive' Class : 1
Subject-level Analysis
RetCam Images
compute_aupr(subject_level$retcam, subject_level$label, 'PR: Raw RetCam Images')
compute_auroc(subject_level$retcam, subject_level$label, 'ROC: Raw RetCam Images')
confusion_matrix(subject_level, 'retcam')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 43 0
1 0 27
Accuracy : 1
95% CI : (0.9487, 1)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 1.534e-15
Kappa : 1
Mcnemar's Test P-Value : NA
Sensitivity : 1.0000
Specificity : 1.0000
Pos Pred Value : 1.0000
Neg Pred Value : 1.0000
Prevalence : 0.3857
Detection Rate : 0.3857
Detection Prevalence : 0.3857
Balanced Accuracy : 1.0000
'Positive' Class : 1
compute_aupr(subject_level$retcam_random, subject_level$label, 'PR: Raw RetCam Images - Shuffled Labels')
compute_auroc(subject_level$retcam_random, subject_level$label, 'ROC: Raw RetCam Images - Shuffled Labels')
confusion_matrix(subject_level, 'retcam_random')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 24 2
1 19 25
Accuracy : 0.7
95% CI : (0.5787, 0.8038)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.0869051
Kappa : 0.4333
Mcnemar's Test P-Value : 0.0004803
Sensitivity : 0.9259
Specificity : 0.5581
Pos Pred Value : 0.5682
Neg Pred Value : 0.9231
Prevalence : 0.3857
Detection Rate : 0.3571
Detection Prevalence : 0.6286
Balanced Accuracy : 0.7420
'Positive' Class : 1
Segmentations: zero all pixels < 0
compute_aupr(subject_level$filter_0, subject_level$label, 'PR: Segmentations: zero all pixels < 0')
compute_auroc(subject_level$filter_0, subject_level$label, 'ROC: Segmentations: zero all pixels < 0')
confusion_matrix(subject_level, 'filter_0')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 42 2
1 1 25
Accuracy : 0.9571
95% CI : (0.8798, 0.9911)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 2.232e-11
Kappa : 0.9089
Mcnemar's Test P-Value : 1
Sensitivity : 0.9259
Specificity : 0.9767
Pos Pred Value : 0.9615
Neg Pred Value : 0.9545
Prevalence : 0.3857
Detection Rate : 0.3571
Detection Prevalence : 0.3714
Balanced Accuracy : 0.9513
'Positive' Class : 1
compute_aupr(subject_level$filter_0_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 0 and binarize')
compute_auroc(subject_level$filter_0_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 0 and binarize')
confusion_matrix(subject_level, 'filter_0_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 41 0
1 2 27
Accuracy : 0.9714
95% CI : (0.9006, 0.9965)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 1.53e-12
Kappa : 0.9405
Mcnemar's Test P-Value : 0.4795
Sensitivity : 1.0000
Specificity : 0.9535
Pos Pred Value : 0.9310
Neg Pred Value : 1.0000
Prevalence : 0.3857
Detection Rate : 0.3857
Detection Prevalence : 0.4143
Balanced Accuracy : 0.9767
'Positive' Class : 1
compute_aupr(subject_level$filter_0_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 0 and skeletonize')
compute_auroc(subject_level$filter_0_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 0 and skeletonize')
confusion_matrix(subject_level, 'filter_0_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 40 0
1 3 27
Accuracy : 0.9571
95% CI : (0.8798, 0.9911)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 2.232e-11
Kappa : 0.9114
Mcnemar's Test P-Value : 0.2482
Sensitivity : 1.0000
Specificity : 0.9302
Pos Pred Value : 0.9000
Neg Pred Value : 1.0000
Prevalence : 0.3857
Detection Rate : 0.3857
Detection Prevalence : 0.4286
Balanced Accuracy : 0.9651
'Positive' Class : 1
compute_aupr(subject_level$filter_0_random, subject_level$label, 'PR: Segmentations: zero all pixels < 0 - Shuffled Labels')
compute_auroc(subject_level$filter_0_random, subject_level$label, 'ROC: Segmentations: zero all pixels < 0 - Shuffled Labels')
confusion_matrix(subject_level, 'filter_0_random')Warning in confusionMatrix.default(preds, df$label, positive = "1") :
Levels are not in the same order for reference and data. Refactoring data to match.Confusion Matrix and Statistics
Reference
Prediction 0 1
0 43 27
1 0 0
Accuracy : 0.6143
95% CI : (0.4903, 0.7283)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.5525
Kappa : 0
Mcnemar's Test P-Value : 5.624e-07
Sensitivity : 0.0000
Specificity : 1.0000
Pos Pred Value : NaN
Neg Pred Value : 0.6143
Prevalence : 0.3857
Detection Rate : 0.0000
Detection Prevalence : 0.0000
Balanced Accuracy : 0.5000
'Positive' Class : 1
Segmentations: zero all pixels < 50
compute_aupr(subject_level$filter_50, subject_level$label, 'PR: Segmentations: zero all pixels < 50')
compute_auroc(subject_level$filter_50, subject_level$label, 'ROC: Segmentations: zero all pixels < 50')
confusion_matrix(subject_level, 'filter_50')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 36 0
1 7 27
Accuracy : 0.9
95% CI : (0.8048, 0.9588)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 8.517e-08
Kappa : 0.7987
Mcnemar's Test P-Value : 0.02334
Sensitivity : 1.0000
Specificity : 0.8372
Pos Pred Value : 0.7941
Neg Pred Value : 1.0000
Prevalence : 0.3857
Detection Rate : 0.3857
Detection Prevalence : 0.4857
Balanced Accuracy : 0.9186
'Positive' Class : 1
compute_aupr(subject_level$filter_50_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 50 and binarize')
compute_auroc(subject_level$filter_50_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 50 and binarize')
confusion_matrix(subject_level, 'filter_0_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 41 0
1 2 27
Accuracy : 0.9714
95% CI : (0.9006, 0.9965)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 1.53e-12
Kappa : 0.9405
Mcnemar's Test P-Value : 0.4795
Sensitivity : 1.0000
Specificity : 0.9535
Pos Pred Value : 0.9310
Neg Pred Value : 1.0000
Prevalence : 0.3857
Detection Rate : 0.3857
Detection Prevalence : 0.4143
Balanced Accuracy : 0.9767
'Positive' Class : 1
compute_aupr(subject_level$filter_50_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 50 and skeletonize')
compute_auroc(subject_level$filter_50_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 50 and skeletonize')
confusion_matrix(subject_level, 'filter_50_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 19 0
1 24 27
Accuracy : 0.6571
95% CI : (0.534, 0.7665)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.2718
Kappa : 0.3792
Mcnemar's Test P-Value : 2.668e-06
Sensitivity : 1.0000
Specificity : 0.4419
Pos Pred Value : 0.5294
Neg Pred Value : 1.0000
Prevalence : 0.3857
Detection Rate : 0.3857
Detection Prevalence : 0.7286
Balanced Accuracy : 0.7209
'Positive' Class : 1
Segmentations: zero all pixels < 100
compute_aupr(subject_level$filter_100, subject_level$label, 'PR: Segmentations: zero all pixels < 100')
compute_auroc(subject_level$filter_100, subject_level$label, 'ROC: Segmentations: zero all pixels < 100')
confusion_matrix(subject_level, 'filter_100')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 43 10
1 0 17
Accuracy : 0.8571
95% CI : (0.7529, 0.9293)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 7.748e-06
Kappa : 0.6762
Mcnemar's Test P-Value : 0.004427
Sensitivity : 0.6296
Specificity : 1.0000
Pos Pred Value : 1.0000
Neg Pred Value : 0.8113
Prevalence : 0.3857
Detection Rate : 0.2429
Detection Prevalence : 0.2429
Balanced Accuracy : 0.8148
'Positive' Class : 1
compute_aupr(subject_level$filter_100_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 100 and binarize')
compute_auroc(subject_level$filter_100_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 100 and binarize')
confusion_matrix(subject_level, 'filter_100_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 39 4
1 4 23
Accuracy : 0.8857
95% CI : (0.7872, 0.9493)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 4.352e-07
Kappa : 0.7588
Mcnemar's Test P-Value : 1
Sensitivity : 0.8519
Specificity : 0.9070
Pos Pred Value : 0.8519
Neg Pred Value : 0.9070
Prevalence : 0.3857
Detection Rate : 0.3286
Detection Prevalence : 0.3857
Balanced Accuracy : 0.8794
'Positive' Class : 1
compute_aupr(subject_level$filter_100_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 100 and skeletonize')
compute_auroc(subject_level$filter_100_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 100 and skeletonize')
confusion_matrix(subject_level, 'filter_100_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 38 3
1 5 24
Accuracy : 0.8857
95% CI : (0.7872, 0.9493)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 4.352e-07
Kappa : 0.7621
Mcnemar's Test P-Value : 0.7237
Sensitivity : 0.8889
Specificity : 0.8837
Pos Pred Value : 0.8276
Neg Pred Value : 0.9268
Prevalence : 0.3857
Detection Rate : 0.3429
Detection Prevalence : 0.4143
Balanced Accuracy : 0.8863
'Positive' Class : 1
Segmentations: zero all pixels < 150
compute_aupr(subject_level$filter_150, subject_level$label, 'PR: Segmentations: zero all pixels < 150')
compute_auroc(subject_level$filter_150, subject_level$label, 'ROC: Segmentations: zero all pixels < 150')
confusion_matrix(subject_level, 'filter_150')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 37 0
1 6 27
Accuracy : 0.9143
95% CI : (0.8227, 0.9679)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 1.438e-08
Kappa : 0.8263
Mcnemar's Test P-Value : 0.04123
Sensitivity : 1.0000
Specificity : 0.8605
Pos Pred Value : 0.8182
Neg Pred Value : 1.0000
Prevalence : 0.3857
Detection Rate : 0.3857
Detection Prevalence : 0.4714
Balanced Accuracy : 0.9302
'Positive' Class : 1
compute_aupr(subject_level$filter_150_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 150 and binarize')
compute_auroc(subject_level$filter_150_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 150 and binarize')
confusion_matrix(subject_level, 'filter_150_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 43 5
1 0 22
Accuracy : 0.9286
95% CI : (0.8411, 0.9764)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 2.054e-09
Kappa : 0.8439
Mcnemar's Test P-Value : 0.07364
Sensitivity : 0.8148
Specificity : 1.0000
Pos Pred Value : 1.0000
Neg Pred Value : 0.8958
Prevalence : 0.3857
Detection Rate : 0.3143
Detection Prevalence : 0.3143
Balanced Accuracy : 0.9074
'Positive' Class : 1
compute_aupr(subject_level$filter_150_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 150 and skeletonize')
compute_auroc(subject_level$filter_150_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 150 and skeletonize')
confusion_matrix(subject_level, 'filter_150_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 38 3
1 5 24
Accuracy : 0.8857
95% CI : (0.7872, 0.9493)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 4.352e-07
Kappa : 0.7621
Mcnemar's Test P-Value : 0.7237
Sensitivity : 0.8889
Specificity : 0.8837
Pos Pred Value : 0.8276
Neg Pred Value : 0.9268
Prevalence : 0.3857
Detection Rate : 0.3429
Detection Prevalence : 0.4143
Balanced Accuracy : 0.8863
'Positive' Class : 1
Segmentations: zero all pixels < 200
compute_aupr(subject_level$filter_200, subject_level$label, 'PR: Segmentations: zero all pixels < 200')
compute_auroc(subject_level$filter_200, subject_level$label, 'ROC: Segmentations: zero all pixels < 200')
confusion_matrix(subject_level, 'filter_200')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 34 4
1 9 23
Accuracy : 0.8143
95% CI : (0.7034, 0.8972)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.0002607
Kappa : 0.6211
Mcnemar's Test P-Value : 0.2672575
Sensitivity : 0.8519
Specificity : 0.7907
Pos Pred Value : 0.7187
Neg Pred Value : 0.8947
Prevalence : 0.3857
Detection Rate : 0.3286
Detection Prevalence : 0.4571
Balanced Accuracy : 0.8213
'Positive' Class : 1
compute_aupr(subject_level$filter_200_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 200 and binarize')
compute_auroc(subject_level$filter_200_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 200 and binarize')
confusion_matrix(subject_level, 'filter_200_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 40 12
1 3 15
Accuracy : 0.7857
95% CI : (0.6713, 0.8748)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.001729
Kappa : 0.5179
Mcnemar's Test P-Value : 0.038867
Sensitivity : 0.5556
Specificity : 0.9302
Pos Pred Value : 0.8333
Neg Pred Value : 0.7692
Prevalence : 0.3857
Detection Rate : 0.2143
Detection Prevalence : 0.2571
Balanced Accuracy : 0.7429
'Positive' Class : 1
compute_aupr(subject_level$filter_200_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 200 and skeletonize')
compute_auroc(subject_level$filter_200_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 200 and skeletonize')
confusion_matrix(subject_level, 'filter_200_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 43 25
1 0 2
Accuracy : 0.6429
95% CI : (0.5193, 0.7539)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.3595
Kappa : 0.0895
Mcnemar's Test P-Value : 1.587e-06
Sensitivity : 0.07407
Specificity : 1.00000
Pos Pred Value : 1.00000
Neg Pred Value : 0.63235
Prevalence : 0.38571
Detection Rate : 0.02857
Detection Prevalence : 0.02857
Balanced Accuracy : 0.53704
'Positive' Class : 1
Segmentations: zero all pixels < 210
compute_aupr(subject_level$filter_210, subject_level$label, 'PR: Segmentations: zero all pixels < 210')
compute_auroc(subject_level$filter_210, subject_level$label, 'ROC: Segmentations: zero all pixels < 210')
confusion_matrix(subject_level, 'filter_210')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 43 20
1 0 7
Accuracy : 0.7143
95% CI : (0.5938, 0.816)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.0532
Kappa : 0.3007
Mcnemar's Test P-Value : 2.152e-05
Sensitivity : 0.2593
Specificity : 1.0000
Pos Pred Value : 1.0000
Neg Pred Value : 0.6825
Prevalence : 0.3857
Detection Rate : 0.1000
Detection Prevalence : 0.1000
Balanced Accuracy : 0.6296
'Positive' Class : 1
compute_aupr(subject_level$filter_210_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 210 and binarize')
compute_auroc(subject_level$filter_210_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 210 and binarize')
confusion_matrix(subject_level, 'filter_210_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 39 9
1 4 18
Accuracy : 0.8143
95% CI : (0.7034, 0.8972)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.0002607
Kappa : 0.5941
Mcnemar's Test P-Value : 0.2672575
Sensitivity : 0.6667
Specificity : 0.9070
Pos Pred Value : 0.8182
Neg Pred Value : 0.8125
Prevalence : 0.3857
Detection Rate : 0.2571
Detection Prevalence : 0.3143
Balanced Accuracy : 0.7868
'Positive' Class : 1
compute_aupr(subject_level$filter_210_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 210 and skeletonize')
compute_auroc(subject_level$filter_210_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 210 and skeletonize')
confusion_matrix(subject_level, 'filter_210_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 35 7
1 8 20
Accuracy : 0.7857
95% CI : (0.6713, 0.8748)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.001729
Kappa : 0.5509
Mcnemar's Test P-Value : 1.000000
Sensitivity : 0.7407
Specificity : 0.8140
Pos Pred Value : 0.7143
Neg Pred Value : 0.8333
Prevalence : 0.3857
Detection Rate : 0.2857
Detection Prevalence : 0.4000
Balanced Accuracy : 0.7773
'Positive' Class : 1
Segmentations: zero all pixels < 220
compute_aupr(subject_level$filter_220, subject_level$label, 'PR: Segmentations: zero all pixels < 220')
compute_auroc(subject_level$filter_220, subject_level$label, 'ROC: Segmentations: zero all pixels < 220')
confusion_matrix(subject_level, 'filter_220')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 34 12
1 9 15
Accuracy : 0.7
95% CI : (0.5787, 0.8038)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.08691
Kappa : 0.3536
Mcnemar's Test P-Value : 0.66252
Sensitivity : 0.5556
Specificity : 0.7907
Pos Pred Value : 0.6250
Neg Pred Value : 0.7391
Prevalence : 0.3857
Detection Rate : 0.2143
Detection Prevalence : 0.3429
Balanced Accuracy : 0.6731
'Positive' Class : 1
compute_aupr(subject_level$filter_220_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 220 and binarize')
compute_auroc(subject_level$filter_220_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 220 and binarize')
confusion_matrix(subject_level, 'filter_220_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 31 7
1 12 20
Accuracy : 0.7286
95% CI : (0.609, 0.828)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.03066
Kappa : 0.4463
Mcnemar's Test P-Value : 0.35880
Sensitivity : 0.7407
Specificity : 0.7209
Pos Pred Value : 0.6250
Neg Pred Value : 0.8158
Prevalence : 0.3857
Detection Rate : 0.2857
Detection Prevalence : 0.4571
Balanced Accuracy : 0.7308
'Positive' Class : 1
compute_aupr(subject_level$filter_220_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 220 and skeletonize')
compute_auroc(subject_level$filter_220_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 220 and skeletonize')
confusion_matrix(subject_level, 'filter_220_skeletonized')Warning in confusionMatrix.default(preds, df$label, positive = "1") :
Levels are not in the same order for reference and data. Refactoring data to match.Confusion Matrix and Statistics
Reference
Prediction 0 1
0 43 27
1 0 0
Accuracy : 0.6143
95% CI : (0.4903, 0.7283)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.5525
Kappa : 0
Mcnemar's Test P-Value : 5.624e-07
Sensitivity : 0.0000
Specificity : 1.0000
Pos Pred Value : NaN
Neg Pred Value : 0.6143
Prevalence : 0.3857
Detection Rate : 0.0000
Detection Prevalence : 0.0000
Balanced Accuracy : 0.5000
'Positive' Class : 1
Segmentations: zero all pixels < 230
compute_aupr(subject_level$filter_230, subject_level$label, 'PR: Segmentations: zero all pixels < 230')
compute_auroc(subject_level$filter_230, subject_level$label, 'ROC: Segmentations: zero all pixels < 230')
confusion_matrix(subject_level, 'filter_230')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 26 6
1 17 21
Accuracy : 0.6714
95% CI : (0.5488, 0.7791)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.19581
Kappa : 0.3555
Mcnemar's Test P-Value : 0.03706
Sensitivity : 0.7778
Specificity : 0.6047
Pos Pred Value : 0.5526
Neg Pred Value : 0.8125
Prevalence : 0.3857
Detection Rate : 0.3000
Detection Prevalence : 0.5429
Balanced Accuracy : 0.6912
'Positive' Class : 1
compute_aupr(subject_level$filter_230_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 230 and binarize')
compute_auroc(subject_level$filter_230_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 230 and binarize')
confusion_matrix(subject_level, 'filter_230_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 10 1
1 33 26
Accuracy : 0.5143
95% CI : (0.3917, 0.6356)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.966
Kappa : 0.1602
Mcnemar's Test P-Value : 1.058e-07
Sensitivity : 0.9630
Specificity : 0.2326
Pos Pred Value : 0.4407
Neg Pred Value : 0.9091
Prevalence : 0.3857
Detection Rate : 0.3714
Detection Prevalence : 0.8429
Balanced Accuracy : 0.5978
'Positive' Class : 1
compute_aupr(subject_level$filter_230_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 230 and skeletonize')
compute_auroc(subject_level$filter_230_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 230 and skeletonize')
confusion_matrix(subject_level, 'filter_230_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 17 2
1 26 25
Accuracy : 0.6
95% CI : (0.4759, 0.7153)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.6466
Kappa : 0.2757
Mcnemar's Test P-Value : 1.383e-05
Sensitivity : 0.9259
Specificity : 0.3953
Pos Pred Value : 0.4902
Neg Pred Value : 0.8947
Prevalence : 0.3857
Detection Rate : 0.3571
Detection Prevalence : 0.7286
Balanced Accuracy : 0.6606
'Positive' Class : 1
Segmentations: zero all pixels < 240
compute_aupr(subject_level$filter_240, subject_level$label, 'PR: Segmentations: zero all pixels < 240')
compute_auroc(subject_level$filter_240, subject_level$label, 'ROC: Segmentations: zero all pixels < 240')
confusion_matrix(subject_level, 'filter_240')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 23 9
1 20 18
Accuracy : 0.5857
95% CI : (0.4617, 0.7023)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.73214
Kappa : 0.1873
Mcnemar's Test P-Value : 0.06332
Sensitivity : 0.6667
Specificity : 0.5349
Pos Pred Value : 0.4737
Neg Pred Value : 0.7187
Prevalence : 0.3857
Detection Rate : 0.2571
Detection Prevalence : 0.5429
Balanced Accuracy : 0.6008
'Positive' Class : 1
compute_aupr(subject_level$filter_240_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 240 and binarize')
compute_auroc(subject_level$filter_240_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 240 and binarize')
confusion_matrix(subject_level, 'filter_240_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 12 1
1 31 26
Accuracy : 0.5429
95% CI : (0.4194, 0.6626)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.9106
Kappa : 0.2006
Mcnemar's Test P-Value : 2.951e-07
Sensitivity : 0.9630
Specificity : 0.2791
Pos Pred Value : 0.4561
Neg Pred Value : 0.9231
Prevalence : 0.3857
Detection Rate : 0.3714
Detection Prevalence : 0.8143
Balanced Accuracy : 0.6210
'Positive' Class : 1
compute_aupr(subject_level$filter_240_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 240 and skeletonize')
compute_auroc(subject_level$filter_240_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 240 and skeletonize')
confusion_matrix(subject_level, 'filter_240_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 14 2
1 29 25
Accuracy : 0.5571
95% CI : (0.4334, 0.6759)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.8651
Kappa : 0.2121
Mcnemar's Test P-Value : 3.016e-06
Sensitivity : 0.9259
Specificity : 0.3256
Pos Pred Value : 0.4630
Neg Pred Value : 0.8750
Prevalence : 0.3857
Detection Rate : 0.3571
Detection Prevalence : 0.7714
Balanced Accuracy : 0.6258
'Positive' Class : 1
Segmentations: zero all pixels < 250
compute_aupr(subject_level$filter_250, subject_level$label, 'PR: Segmentations: zero all pixels < 250')
compute_auroc(subject_level$filter_250, subject_level$label, 'ROC: Segmentations: zero all pixels < 250')
confusion_matrix(subject_level, 'filter_250')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 1 0
1 42 27
Accuracy : 0.4
95% CI : (0.2847, 0.5241)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.9999
Kappa : 0.018
Mcnemar's Test P-Value : 2.509e-10
Sensitivity : 1.00000
Specificity : 0.02326
Pos Pred Value : 0.39130
Neg Pred Value : 1.00000
Prevalence : 0.38571
Detection Rate : 0.38571
Detection Prevalence : 0.98571
Balanced Accuracy : 0.51163
'Positive' Class : 1
compute_aupr(subject_level$filter_250_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 250 and binarize')
compute_auroc(subject_level$filter_250_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 250 and binarize')
confusion_matrix(subject_level, 'filter_250_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 1 0
1 42 27
Accuracy : 0.4
95% CI : (0.2847, 0.5241)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.9999
Kappa : 0.018
Mcnemar's Test P-Value : 2.509e-10
Sensitivity : 1.00000
Specificity : 0.02326
Pos Pred Value : 0.39130
Neg Pred Value : 1.00000
Prevalence : 0.38571
Detection Rate : 0.38571
Detection Prevalence : 0.98571
Balanced Accuracy : 0.51163
'Positive' Class : 1
compute_aupr(subject_level$filter_250_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 250 and skeletonize')
compute_auroc(subject_level$filter_250_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 250 and skeletonize')
confusion_matrix(subject_level, 'filter_250_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 1 0
1 42 27
Accuracy : 0.4
95% CI : (0.2847, 0.5241)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.9999
Kappa : 0.018
Mcnemar's Test P-Value : 2.509e-10
Sensitivity : 1.00000
Specificity : 0.02326
Pos Pred Value : 0.39130
Neg Pred Value : 1.00000
Prevalence : 0.38571
Detection Rate : 0.38571
Detection Prevalence : 0.98571
Balanced Accuracy : 0.51163
'Positive' Class : 1
Segmentations: zero all pixels < 257
compute_aupr(subject_level$filter_257, subject_level$label, 'PR: Segmentations: zero all pixels < 257')
compute_auroc(subject_level$filter_257, subject_level$label, 'ROC: Segmentations: zero all pixels < 257')
confusion_matrix(subject_level, 'filter_257')Warning in confusionMatrix.default(preds, df$label, positive = "1") :
Levels are not in the same order for reference and data. Refactoring data to match.Confusion Matrix and Statistics
Reference
Prediction 0 1
0 43 27
1 0 0
Accuracy : 0.6143
95% CI : (0.4903, 0.7283)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.5525
Kappa : 0
Mcnemar's Test P-Value : 5.624e-07
Sensitivity : 0.0000
Specificity : 1.0000
Pos Pred Value : NaN
Neg Pred Value : 0.6143
Prevalence : 0.3857
Detection Rate : 0.0000
Detection Prevalence : 0.0000
Balanced Accuracy : 0.5000
'Positive' Class : 1
compute_aupr(subject_level$filter_257_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 257 and binarize')
compute_auroc(subject_level$filter_257_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 257 and binarize')
confusion_matrix(subject_level, 'filter_257_binarized')Warning in confusionMatrix.default(preds, df$label, positive = "1") :
Levels are not in the same order for reference and data. Refactoring data to match.Confusion Matrix and Statistics
Reference
Prediction 0 1
0 43 27
1 0 0
Accuracy : 0.6143
95% CI : (0.4903, 0.7283)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.5525
Kappa : 0
Mcnemar's Test P-Value : 5.624e-07
Sensitivity : 0.0000
Specificity : 1.0000
Pos Pred Value : NaN
Neg Pred Value : 0.6143
Prevalence : 0.3857
Detection Rate : 0.0000
Detection Prevalence : 0.0000
Balanced Accuracy : 0.5000
'Positive' Class : 1
compute_aupr(subject_level$filter_257_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 257 and skeletonize')
compute_auroc(subject_level$filter_257_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 257 and skeletonize')
confusion_matrix(subject_level, 'filter_257_skeletonized')Warning in confusionMatrix.default(preds, df$label, positive = "1") :
Levels are not in the same order for reference and data. Refactoring data to match.Confusion Matrix and Statistics
Reference
Prediction 0 1
0 43 27
1 0 0
Accuracy : 0.6143
95% CI : (0.4903, 0.7283)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.5525
Kappa : 0
Mcnemar's Test P-Value : 5.624e-07
Sensitivity : 0.0000
Specificity : 1.0000
Pos Pred Value : NaN
Neg Pred Value : 0.6143
Prevalence : 0.3857
Detection Rate : 0.0000
Detection Prevalence : 0.0000
Balanced Accuracy : 0.5000
'Positive' Class : 1
Segmentations: zero all pixels > 10
compute_aupr(subject_level$filter_10, subject_level$label, 'PR: Segmentations: zero all pixels < 10')
compute_auroc(subject_level$filter_10, subject_level$label, 'ROC: Segmentations: zero all pixels < 10')
confusion_matrix(subject_level, 'filter_10')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 41 8
1 2 19
Accuracy : 0.8571
95% CI : (0.7529, 0.9293)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 7.748e-06
Kappa : 0.6855
Mcnemar's Test P-Value : 0.1138
Sensitivity : 0.7037
Specificity : 0.9535
Pos Pred Value : 0.9048
Neg Pred Value : 0.8367
Prevalence : 0.3857
Detection Rate : 0.2714
Detection Prevalence : 0.3000
Balanced Accuracy : 0.8286
'Positive' Class : 1
compute_aupr(subject_level$filter_10_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 10 and binarize')
compute_auroc(subject_level$filter_10_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 10 and binarize')
confusion_matrix(subject_level, 'filter_10_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 34 0
1 9 27
Accuracy : 0.8714
95% CI : (0.7699, 0.9395)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 1.949e-06
Kappa : 0.7445
Mcnemar's Test P-Value : 0.007661
Sensitivity : 1.0000
Specificity : 0.7907
Pos Pred Value : 0.7500
Neg Pred Value : 1.0000
Prevalence : 0.3857
Detection Rate : 0.3857
Detection Prevalence : 0.5143
Balanced Accuracy : 0.8953
'Positive' Class : 1
compute_aupr(subject_level$filter_10_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 10 and skeletonize')
compute_auroc(subject_level$filter_10_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 10 and skeletonize')
confusion_matrix(subject_level, 'filter_10_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 39 0
1 4 27
Accuracy : 0.9429
95% CI : (0.8601, 0.9842)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 2.41e-10
Kappa : 0.8826
Mcnemar's Test P-Value : 0.1336
Sensitivity : 1.0000
Specificity : 0.9070
Pos Pred Value : 0.8710
Neg Pred Value : 1.0000
Prevalence : 0.3857
Detection Rate : 0.3857
Detection Prevalence : 0.4429
Balanced Accuracy : 0.9535
'Positive' Class : 1
Segmentations: zero all pixels < 75 and > 150
compute_aupr(subject_level$filter_75, subject_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150')
compute_auroc(subject_level$filter_75, subject_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150')
confusion_matrix(subject_level, 'filter_75')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 36 1
1 7 26
Accuracy : 0.8857
95% CI : (0.7872, 0.9493)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 4.352e-07
Kappa : 0.7684
Mcnemar's Test P-Value : 0.0771
Sensitivity : 0.9630
Specificity : 0.8372
Pos Pred Value : 0.7879
Neg Pred Value : 0.9730
Prevalence : 0.3857
Detection Rate : 0.3714
Detection Prevalence : 0.4714
Balanced Accuracy : 0.9001
'Positive' Class : 1
compute_aupr(subject_level$filter_75_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and binarize')
compute_auroc(subject_level$filter_75_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and binarize')
confusion_matrix(subject_level, 'filter_75_binarized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 33 0
1 10 27
Accuracy : 0.8571
95% CI : (0.7529, 0.9293)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 7.748e-06
Kappa : 0.718
Mcnemar's Test P-Value : 0.004427
Sensitivity : 1.0000
Specificity : 0.7674
Pos Pred Value : 0.7297
Neg Pred Value : 1.0000
Prevalence : 0.3857
Detection Rate : 0.3857
Detection Prevalence : 0.5286
Balanced Accuracy : 0.8837
'Positive' Class : 1
compute_aupr(subject_level$filter_75_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and skeletonize')
compute_auroc(subject_level$filter_75_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and skeletonize')
confusion_matrix(subject_level, 'filter_75_skeletonized')Confusion Matrix and Statistics
Reference
Prediction 0 1
0 43 13
1 0 14
Accuracy : 0.8143
95% CI : (0.7034, 0.8972)
No Information Rate : 0.6143
P-Value [Acc > NIR] : 0.0002607
Kappa : 0.5695
Mcnemar's Test P-Value : 0.0008741
Sensitivity : 0.5185
Specificity : 1.0000
Pos Pred Value : 1.0000
Neg Pred Value : 0.7679
Prevalence : 0.3857
Detection Rate : 0.2000
Detection Prevalence : 0.2000
Balanced Accuracy : 0.7593
'Positive' Class : 1
aupr_figures <- function(predictions, labels, title, names, save_name) {
n_black <- length(labels[labels == 1])
n_total <- length(labels)
for (i in seq_along(predictions)) {
preds <- prediction(predictions[i], labels)
aupr <- performance(preds, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
perf <- performance(preds, 'prec', 'rec')
if (is.null(dev.list())) {
png(save_name,
width=7,
height=5,
units='in',
res=300)
plot(perf,
main = title,
xlim=c(0,1),
ylim=c(0,1),
col = i,
lwd = 2)
} else {
plot(perf,
main = title,
xlim=c(0,1),
ylim=c(0,1),
col = i,
lwd = 2,
add = TRUE)
}
}
abline(a = n_black/n_total,
b = 0,
col = 'red',
lty = 2,
lwd = 2)
legend('bottomleft',
names,
lwd = 2,
col = seq_along(predictions),
bty = 'n',
inset = c(0.1, 0.07))
legend('bottomleft',
paste('Null AUPR:', sprintf('%.3f', round(n_black / n_total, 3))),
lwd = 2,
lty = 2,
col = 'red',
bty = 'n',
inset = c(0.1, 0.0))
}Figures for Paper
aupr_figures(list(image_level$filter_0, image_level$filter_50, image_level$filter_200, image_level$filter_240),
image_level$label,
'Precision-Recall Curves of Thresholded RVMs',
c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
'./out/figures/thresholded_image.png')aupr_figures(list(image_level$filter_0_binarized, image_level$filter_50_binarized, image_level$filter_200_binarized, image_level$filter_240_binarized),
image_level$label,
'Precision-Recall Curves of Binarized RVMs',
c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
'./out/figures/binarized_image.png')aupr_figures(list(image_level$filter_0_skeletonized, image_level$filter_50_skeletonized, image_level$filter_200_skeletonized, image_level$filter_240_skeletonized),
image_level$label,
'Precision-Recall Curves of Skeletonized RVMs',
c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
'./out/figures/skeletonized_image.png')aupr_figures(list(subject_level$filter_0, subject_level$filter_50, subject_level$filter_200, subject_level$filter_240),
subject_level$label,
'Precision-Recall Curves of Thresholded RVMs',
c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
'./out/figures/thresholded_subject.png')aupr_figures(list(subject_level$filter_0_binarized, subject_level$filter_50_binarized, subject_level$filter_200_binarized, subject_level$filter_240_binarized),
subject_level$label,
'Precision-Recall Curves of Binarized RVMs',
c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
'./out/figures/binarized_subject.png')aupr_figures(list(subject_level$filter_0_skeletonized, subject_level$filter_50_skeletonized, subject_level$filter_200_skeletonized, subject_level$filter_240_skeletonized),
subject_level$label,
'Precision-Recall Curves of Skeletonized RVMs',
c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
'./out/figures/skeletonized_subject.png')Evaluate on Manual Segmentations
manual_binary_0 <- read_csv('./out/probabilities/manual_segmentations_binarized_0.csv') %>%
mutate(img_name = basename(img_loc)) %>%
separate(img_name, c('site', 'subject_id')) %>%
filter(!is.na(as.numeric(subject_id))) %>%
mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
select(subject_id, probability) %>%
inner_join(irop_data) %>%
filter(race == 'African American' | race == 'Caucasian/White') %>%
mutate(race = if_else(race == 'African American', 1, 0),
across(race, as.factor))
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
img_loc = col_character(),
probability = col_double()
)Warning: Expected 2 pieces. Additional pieces discarded in 196 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
Warning in mask$eval_all_filter(dots, env_filter) :
NAs introduced by coercionJoining, by = "subject_id"manual_skeleton_0 <- read_csv('./out/probabilities/manual_segmentations_skeletonized_0.csv') %>%
mutate(img_name = basename(img_loc)) %>%
separate(img_name, c('site', 'subject_id')) %>%
filter(!is.na(as.numeric(subject_id))) %>%
mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
select(subject_id, probability) %>%
inner_join(irop_data) %>%
filter(race == 'African American' | race == 'Caucasian/White') %>%
mutate(race = if_else(race == 'African American', 1, 0),
across(race, as.factor))
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
img_loc = col_character(),
probability = col_double()
)Warning: Expected 2 pieces. Additional pieces discarded in 196 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
Warning in mask$eval_all_filter(dots, env_filter) :
NAs introduced by coercionJoining, by = "subject_id"manual_binary_50 <- read_csv('./out/probabilities/manual_segmentations_binarized_50.csv') %>%
mutate(img_name = basename(img_loc)) %>%
separate(img_name, c('site', 'subject_id')) %>%
filter(!is.na(as.numeric(subject_id))) %>%
mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
select(subject_id, probability) %>%
inner_join(irop_data) %>%
filter(race == 'African American' | race == 'Caucasian/White') %>%
mutate(race = if_else(race == 'African American', 1, 0),
across(race, as.factor))
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
img_loc = col_character(),
probability = col_double()
)Warning: Expected 2 pieces. Additional pieces discarded in 196 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
Warning in mask$eval_all_filter(dots, env_filter) :
NAs introduced by coercionJoining, by = "subject_id"manual_skeleton_50 <- read_csv('./out/probabilities/manual_segmentations_skeletonized_50.csv') %>%
mutate(img_name = basename(img_loc)) %>%
separate(img_name, c('site', 'subject_id')) %>%
filter(!is.na(as.numeric(subject_id))) %>%
mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
select(subject_id, probability) %>%
inner_join(irop_data) %>%
filter(race == 'African American' | race == 'Caucasian/White') %>%
mutate(race = if_else(race == 'African American', 1, 0),
across(race, as.factor))
── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
img_loc = col_character(),
probability = col_double()
)Warning: Expected 2 pieces. Additional pieces discarded in 196 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
Warning in mask$eval_all_filter(dots, env_filter) :
NAs introduced by coercionJoining, by = "subject_id"pred <- prediction(manual_binary_0$probability, manual_binary_0$race)
num_yes <- length(manual_binary_0$race[manual_binary_0$race == 1])
num_no <- length(manual_binary_0$race[manual_binary_0$race == 0])
perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Binarized - 0 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))
pred <- prediction(manual_skeleton_0$probability, manual_skeleton_0$race)
num_yes <- length(manual_skeleton_0$race[manual_skeleton_0$race == 1])
num_no <- length(manual_skeleton_0$race[manual_skeleton_0$race == 0])
perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Skeletonized - 0 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))
pred <- prediction(manual_binary_50$probability, manual_binary_50$race)
num_yes <- length(manual_binary_50$race[manual_binary_50$race == 1])
num_no <- length(manual_binary_50$race[manual_binary_50$race == 0])
perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Binarized - 50 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))
pred <- prediction(manual_skeleton_50$probability, manual_skeleton_50$race)
num_yes <- length(manual_skeleton_50$race[manual_skeleton_50$race == 1])
num_no <- length(manual_skeleton_50$race[manual_skeleton_50$race == 0])
perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Skeletonized - 50 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))
image_level <- read_csv('./out/probabilities/retcam_filtered_0.csv', col_types = cols()) %>%
mutate(label = as.factor(if_else(str_detect(img_loc, 'black'), 1, 0)),
image_id = as.numeric(file_path_sans_ext(basename(img_loc)))) %>%
select(img_loc, image_id, label, retcam = probability) %>%
bind_cols(select(read_csv('./out/probabilities/segmentations_binarized_test.csv', col_types = cols()), filter_0_binarized = probability))pred <- prediction(image_level$filter_0_binarized, image_level$label)
num_yes <- length(image_level$label[image_level$label == 1])
num_no <- length(image_level$label[image_level$label == 0])
perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Skeletonized - 0 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))
---
title: 'Evaluate Models'
author: 'Author: Aaron S Coyner, PhD'
date: 'Last update: `r Sys.Date()`'
output:
    html_notebook:
        toc: yes
        toc_float: yes
        toc_depth: 3
---


# Setup
```{r}
library(tidyverse)
library(janitor)
library(tools)
library(caret)
library(ROCR)
library(caret)
```


```{r}
compute_auroc <- function(predictions, labels, title) {
    pred <- prediction(predictions, labels)
    auroc <- performance(pred, measure = 'auc')
    auroc <- auroc@y.values[[1]]
    perf <- performance(pred, 'tpr', 'fpr')
    plot(perf,
         main = title,
         xlim=c(0,1),
         ylim=c(0,1),
         colorize = FALSE)
    abline(a = 0,
           b = 1,
           col = 'red',
           lty = 2)
    text(0.8,
         0.25,
         paste('AUROC:', sprintf('%.3f', round(auroc, 3))))
}
```


```{r}
compute_aupr <- function(predictions, labels, title) {
    n_black <- length(labels[labels == 1])
    n_total <- length(labels)
    pred <- prediction(predictions, labels)
    aupr <- performance(pred, measure = 'aucpr')
    aupr <- aupr@y.values[[1]]
    perf <- performance(pred, 'prec', 'rec')
    plot(perf,
         main = title,
         xlim=c(0,1),
         ylim=c(0,1),
         colorize = FALSE)
    abline(a = n_black/n_total,
           b = 0,
           col = 'red',
           lty = 2)
    text(0.2,
         0.2,
         paste('Null AUPR:', sprintf('%.3f', round(n_black / n_total, 3))))
    text(0.8,
         0.2,
         paste('AUPR:', sprintf('%.3f', round(aupr, 3))))
}
```


```{r}
confusion_matrix <- function(df, column, threshold=0.5) {
    preds <- as.factor(if_else(df[column] >= threshold, 1, 0))
    confusionMatrix(preds, df$label, positive = '1')
}
```



# Load data
```{r}
irop_data <- read_csv('/Volumes/External/irop_data/irop_07092020.csv') %>%
    clean_names() %>%
    distinct(subject_id, .keep_all = TRUE) %>%
    select(subject_id, race)

test_data <- read_csv('./out/datasets/test_data.csv') %>%
    select(subject_id, image_id)


image_level <- read_csv('./out/probabilities/retcam_filtered_0.csv', col_types = cols()) %>%
    mutate(label = as.factor(if_else(str_detect(img_loc, 'black'), 1, 0)),
           image_id = as.numeric(file_path_sans_ext(basename(img_loc)))) %>%
    select(img_loc, image_id, label, retcam = probability) %>%
    bind_cols(select(read_csv('./out/probabilities/retcam_filtered_0_random.csv', col_types = cols()), retcam_random = probability)) %>%
    
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_random.csv', col_types = cols()), filter_0_random = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0.csv', col_types = cols()), filter_0 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_binarized.csv', col_types = cols()), filter_0_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_skeletonized.csv', col_types = cols()), filter_0_skeletonized = probability)) %>%
    
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_50.csv', col_types = cols()), filter_50 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_50_binarized.csv', col_types = cols()), filter_50_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_50_skeletonized.csv', col_types = cols()), filter_50_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_100.csv', col_types = cols()), filter_100 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_100_binarized.csv', col_types = cols()), filter_100_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_100_skeletonized.csv', col_types = cols()), filter_100_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_150.csv', col_types = cols()), filter_150 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_150_binarized.csv', col_types = cols()), filter_150_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_150_skeletonized.csv', col_types = cols()), filter_150_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_200.csv', col_types = cols()), filter_200 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_200_binarized.csv', col_types = cols()), filter_200_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_200_skeletonized.csv', col_types = cols()), filter_200_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_210.csv', col_types = cols()), filter_210 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_210_binarized.csv', col_types = cols()), filter_210_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_210_skeletonized.csv', col_types = cols()), filter_210_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_220.csv', col_types = cols()), filter_220 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_220_binarized.csv', col_types = cols()), filter_220_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_220_skeletonized.csv', col_types = cols()), filter_220_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_230.csv', col_types = cols()), filter_230 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_230_binarized.csv', col_types = cols()), filter_230_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_230_skeletonized.csv', col_types = cols()), filter_230_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_240.csv', col_types = cols()), filter_240 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_240_binarized.csv', col_types = cols()), filter_240_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_240_skeletonized.csv', col_types = cols()), filter_240_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_250.csv', col_types = cols()), filter_250 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_250_binarized.csv', col_types = cols()), filter_250_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_250_skeletonized.csv', col_types = cols()), filter_250_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_257.csv', col_types = cols()), filter_257 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_257_binarized.csv', col_types = cols()), filter_257_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_257_skeletonized.csv', col_types = cols()), filter_257_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_10.csv', col_types = cols()), filter_10 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_10_binarized.csv', col_types = cols()), filter_10_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_0_10_skeletonized.csv', col_types = cols()), filter_10_skeletonized = probability)) %>%

    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_75_150.csv', col_types = cols()), filter_75 = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_75_150_binarized.csv', col_types = cols()), filter_75_binarized = probability)) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_filtered_75_150_skeletonized.csv', col_types = cols()), filter_75_skeletonized = probability)) %>%
    
    inner_join(test_data, by = 'image_id') %>%
    select(subject_id, everything(), -img_loc)



subject_level <- image_level %>%
    group_by(subject_id) %>%
    mutate(across(c(-image_id, -label), median)) %>%
    ungroup() %>%
    distinct(subject_id, .keep_all = TRUE)
```


# Image-level Analysis

### RetCam Images

```{r}
compute_aupr(image_level$retcam, image_level$label, 'PR: Raw RetCam Images')
compute_auroc(image_level$retcam, image_level$label, 'ROC: Raw RetCam Images')
confusion_matrix(image_level, 'retcam')
```

```{r}
compute_aupr(image_level$retcam_random, image_level$label, 'PR: Raw RetCam Images - Shuffled Labels')
compute_auroc(image_level$retcam_random, image_level$label, 'ROC: Raw RetCam Images - Shuffled Labels')
confusion_matrix(image_level, 'retcam_random')
```



### Segmentations: zero all pixels < 0

```{r}
compute_aupr(image_level$filter_0, image_level$label, 'PR: Segmentations: zero all pixels < 0')
compute_auroc(image_level$filter_0, image_level$label, 'ROC: Segmentations: zero all pixels < 0')
confusion_matrix(image_level, 'filter_0')
```

```{r}
compute_aupr(image_level$filter_0_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 0 and binarize')
compute_auroc(image_level$filter_0_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 0 and binarize')
confusion_matrix(image_level, 'filter_0_binarized')
```

```{r}
compute_aupr(image_level$filter_0_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 0 and skeletonize')
compute_auroc(image_level$filter_0_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 0 and skeletonize')
confusion_matrix(image_level, 'filter_0_skeletonized')
```

```{r}
compute_aupr(image_level$filter_0_random, image_level$label, 'PR: Segmentations: zero all pixels < 0 - Shuffled Labels')
compute_auroc(image_level$filter_0_random, image_level$label, 'ROC: Segmentations: zero all pixels < 0 - Shuffled Labels')
confusion_matrix(image_level, 'filter_0_random')
```


### Segmentations: zero all pixels < 50

```{r}
compute_aupr(image_level$filter_50, image_level$label, 'PR: Segmentations: zero all pixels < 50')
compute_auroc(image_level$filter_50, image_level$label, 'ROC: Segmentations: zero all pixels < 50')
confusion_matrix(image_level, 'filter_50')
```

```{r}
compute_aupr(image_level$filter_50_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 50 and binarize')
compute_auroc(image_level$filter_50_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 50 and binarize')
confusion_matrix(image_level, 'filter_0_binarized')
```

```{r}
compute_aupr(image_level$filter_50_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 50 and skeletonize')
compute_auroc(image_level$filter_50_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 50 and skeletonize')
confusion_matrix(image_level, 'filter_50_skeletonized')
```



### Segmentations: zero all pixels < 100

```{r}
compute_aupr(image_level$filter_100, image_level$label, 'PR: Segmentations: zero all pixels < 100')
compute_auroc(image_level$filter_100, image_level$label, 'ROC: Segmentations: zero all pixels < 100')
confusion_matrix(image_level, 'filter_100')
```

```{r}
compute_aupr(image_level$filter_100_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 100 and binarize')
compute_auroc(image_level$filter_100_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 100 and binarize')
confusion_matrix(image_level, 'filter_100_binarized')
```

```{r}
compute_aupr(image_level$filter_100_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 100 and skeletonize')
compute_auroc(image_level$filter_100_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 100 and skeletonize')
confusion_matrix(image_level, 'filter_100_skeletonized')
```



### Segmentations: zero all pixels < 150

```{r}
compute_aupr(image_level$filter_150, image_level$label, 'PR: Segmentations: zero all pixels < 150')
compute_auroc(image_level$filter_150, image_level$label, 'ROC: Segmentations: zero all pixels < 150')
confusion_matrix(image_level, 'filter_150')
```

```{r}
compute_aupr(image_level$filter_150_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 150 and binarize')
compute_auroc(image_level$filter_150_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 150 and binarize')
confusion_matrix(image_level, 'filter_150_binarized')
```

```{r}
compute_aupr(image_level$filter_150_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 150 and skeletonize')
compute_auroc(image_level$filter_150_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 150 and skeletonize')
confusion_matrix(image_level, 'filter_150_skeletonized')
```



### Segmentations: zero all pixels < 200

```{r}
compute_aupr(image_level$filter_200, image_level$label, 'PR: Segmentations: zero all pixels < 200')
compute_auroc(image_level$filter_200, image_level$label, 'ROC: Segmentations: zero all pixels < 200')
confusion_matrix(image_level, 'filter_200')
```

```{r}
compute_aupr(image_level$filter_200_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 200 and binarize')
compute_auroc(image_level$filter_200_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 200 and binarize')
confusion_matrix(image_level, 'filter_200_binarized')
```

```{r}
compute_aupr(image_level$filter_200_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 200 and skeletonize')
compute_auroc(image_level$filter_200_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 200 and skeletonize')
confusion_matrix(image_level, 'filter_200_skeletonized')
```



### Segmentations: zero all pixels < 210

```{r}
compute_aupr(image_level$filter_210, image_level$label, 'PR: Segmentations: zero all pixels < 210')
compute_auroc(image_level$filter_210, image_level$label, 'ROC: Segmentations: zero all pixels < 210')
confusion_matrix(image_level, 'filter_210')
```

```{r}
compute_aupr(image_level$filter_210_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 210 and binarize')
compute_auroc(image_level$filter_210_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 210 and binarize')
confusion_matrix(image_level, 'filter_210_binarized')
```

```{r}
compute_aupr(image_level$filter_210_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 210 and skeletonize')
compute_auroc(image_level$filter_210_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 210 and skeletonize')
confusion_matrix(image_level, 'filter_210_skeletonized')
```



### Segmentations: zero all pixels < 220

```{r}
compute_aupr(image_level$filter_220, image_level$label, 'PR: Segmentations: zero all pixels < 220')
compute_auroc(image_level$filter_220, image_level$label, 'ROC: Segmentations: zero all pixels < 220')
confusion_matrix(image_level, 'filter_220')
```

```{r}
compute_aupr(image_level$filter_220_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 220 and binarize')
compute_auroc(image_level$filter_220_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 220 and binarize')
confusion_matrix(image_level, 'filter_220_binarized')
```

```{r}
compute_aupr(image_level$filter_220_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 220 and skeletonize')
compute_auroc(image_level$filter_220_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 220 and skeletonize')
confusion_matrix(image_level, 'filter_220_skeletonized')
```



### Segmentations: zero all pixels < 230

```{r}
compute_aupr(image_level$filter_230, image_level$label, 'PR: Segmentations: zero all pixels < 230')
compute_auroc(image_level$filter_230, image_level$label, 'ROC: Segmentations: zero all pixels < 230')
confusion_matrix(image_level, 'filter_230')
```

```{r}
compute_aupr(image_level$filter_230_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 230 and binarize')
compute_auroc(image_level$filter_230_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 230 and binarize')
confusion_matrix(image_level, 'filter_230_binarized')
```

```{r}
compute_aupr(image_level$filter_230_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 230 and skeletonize')
compute_auroc(image_level$filter_230_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 230 and skeletonize')
confusion_matrix(image_level, 'filter_230_skeletonized')
```



### Segmentations: zero all pixels < 240

```{r}
compute_aupr(image_level$filter_240, image_level$label, 'PR: Segmentations: zero all pixels < 240')
compute_auroc(image_level$filter_240, image_level$label, 'ROC: Segmentations: zero all pixels < 240')
confusion_matrix(image_level, 'filter_240')
```

```{r}
compute_aupr(image_level$filter_240_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 240 and binarize')
compute_auroc(image_level$filter_240_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 240 and binarize')
confusion_matrix(image_level, 'filter_240_binarized')
```

```{r}
compute_aupr(image_level$filter_240_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 240 and skeletonize')
compute_auroc(image_level$filter_240_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 240 and skeletonize')
confusion_matrix(image_level, 'filter_240_skeletonized')
```



### Segmentations: zero all pixels < 250

```{r}
compute_aupr(image_level$filter_250, image_level$label, 'PR: Segmentations: zero all pixels < 250')
compute_auroc(image_level$filter_250, image_level$label, 'ROC: Segmentations: zero all pixels < 250')
confusion_matrix(image_level, 'filter_250')
```

```{r}
compute_aupr(image_level$filter_250_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 250 and binarize')
compute_auroc(image_level$filter_250_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 250 and binarize')
confusion_matrix(image_level, 'filter_250_binarized')
```

```{r}
compute_aupr(image_level$filter_250_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 250 and skeletonize')
compute_auroc(image_level$filter_250_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 250 and skeletonize')
confusion_matrix(image_level, 'filter_250_skeletonized')
```



### Segmentations: zero all pixels < 257

```{r}
compute_aupr(image_level$filter_257, image_level$label, 'PR: Segmentations: zero all pixels < 257')
compute_auroc(image_level$filter_257, image_level$label, 'ROC: Segmentations: zero all pixels < 257')
confusion_matrix(image_level, 'filter_257')
```

```{r}
compute_aupr(image_level$filter_257_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 257 and binarize')
compute_auroc(image_level$filter_257_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 257 and binarize')
confusion_matrix(image_level, 'filter_257_binarized')
```

```{r}
compute_aupr(image_level$filter_257_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 257 and skeletonize')
compute_auroc(image_level$filter_257_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 257 and skeletonize')
confusion_matrix(image_level, 'filter_257_skeletonized')
```



### Segmentations: zero all pixels > 10

```{r}
compute_aupr(image_level$filter_10, image_level$label, 'PR: Segmentations: zero all pixels < 10')
compute_auroc(image_level$filter_10, image_level$label, 'ROC: Segmentations: zero all pixels < 10')
confusion_matrix(image_level, 'filter_10')
```

```{r}
compute_aupr(image_level$filter_10_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 10 and binarize')
compute_auroc(image_level$filter_10_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 10 and binarize')
confusion_matrix(image_level, 'filter_10_binarized')
```

```{r}
compute_aupr(image_level$filter_10_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 10 and skeletonize')
compute_auroc(image_level$filter_10_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 10 and skeletonize')
confusion_matrix(image_level, 'filter_10_skeletonized')
```



### Segmentations: zero all pixels < 75 and > 150

```{r}
compute_aupr(image_level$filter_75, image_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150')
compute_auroc(image_level$filter_75, image_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150')
confusion_matrix(image_level, 'filter_75')
```

```{r}
compute_aupr(image_level$filter_75_binarized, image_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and binarize')
compute_auroc(image_level$filter_75_binarized, image_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and binarize')
confusion_matrix(image_level, 'filter_75_binarized')
```

```{r}
compute_aupr(image_level$filter_75_skeletonized, image_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and skeletonize')
compute_auroc(image_level$filter_75_skeletonized, image_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and skeletonize')
confusion_matrix(image_level, 'filter_75_skeletonized')
```











# Subject-level Analysis

### RetCam Images

```{r}
compute_aupr(subject_level$retcam, subject_level$label, 'PR: Raw RetCam Images')
compute_auroc(subject_level$retcam, subject_level$label, 'ROC: Raw RetCam Images')
confusion_matrix(subject_level, 'retcam')
```

```{r}
compute_aupr(subject_level$retcam_random, subject_level$label, 'PR: Raw RetCam Images - Shuffled Labels')
compute_auroc(subject_level$retcam_random, subject_level$label, 'ROC: Raw RetCam Images - Shuffled Labels')
confusion_matrix(subject_level, 'retcam_random')
```



### Segmentations: zero all pixels < 0

```{r}
compute_aupr(subject_level$filter_0, subject_level$label, 'PR: Segmentations: zero all pixels < 0')
compute_auroc(subject_level$filter_0, subject_level$label, 'ROC: Segmentations: zero all pixels < 0')
confusion_matrix(subject_level, 'filter_0')
```

```{r}
compute_aupr(subject_level$filter_0_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 0 and binarize')
compute_auroc(subject_level$filter_0_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 0 and binarize')
confusion_matrix(subject_level, 'filter_0_binarized')
```

```{r}
compute_aupr(subject_level$filter_0_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 0 and skeletonize')
compute_auroc(subject_level$filter_0_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 0 and skeletonize')
confusion_matrix(subject_level, 'filter_0_skeletonized')
```

```{r}
compute_aupr(subject_level$filter_0_random, subject_level$label, 'PR: Segmentations: zero all pixels < 0 - Shuffled Labels')
compute_auroc(subject_level$filter_0_random, subject_level$label, 'ROC: Segmentations: zero all pixels < 0 - Shuffled Labels')
confusion_matrix(subject_level, 'filter_0_random')
```


### Segmentations: zero all pixels < 50

```{r}
compute_aupr(subject_level$filter_50, subject_level$label, 'PR: Segmentations: zero all pixels < 50')
compute_auroc(subject_level$filter_50, subject_level$label, 'ROC: Segmentations: zero all pixels < 50')
confusion_matrix(subject_level, 'filter_50')
```

```{r}
compute_aupr(subject_level$filter_50_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 50 and binarize')
compute_auroc(subject_level$filter_50_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 50 and binarize')
confusion_matrix(subject_level, 'filter_0_binarized')
```

```{r}
compute_aupr(subject_level$filter_50_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 50 and skeletonize')
compute_auroc(subject_level$filter_50_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 50 and skeletonize')
confusion_matrix(subject_level, 'filter_50_skeletonized')
```



### Segmentations: zero all pixels < 100

```{r}
compute_aupr(subject_level$filter_100, subject_level$label, 'PR: Segmentations: zero all pixels < 100')
compute_auroc(subject_level$filter_100, subject_level$label, 'ROC: Segmentations: zero all pixels < 100')
confusion_matrix(subject_level, 'filter_100')
```

```{r}
compute_aupr(subject_level$filter_100_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 100 and binarize')
compute_auroc(subject_level$filter_100_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 100 and binarize')
confusion_matrix(subject_level, 'filter_100_binarized')
```

```{r}
compute_aupr(subject_level$filter_100_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 100 and skeletonize')
compute_auroc(subject_level$filter_100_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 100 and skeletonize')
confusion_matrix(subject_level, 'filter_100_skeletonized')
```



### Segmentations: zero all pixels < 150

```{r}
compute_aupr(subject_level$filter_150, subject_level$label, 'PR: Segmentations: zero all pixels < 150')
compute_auroc(subject_level$filter_150, subject_level$label, 'ROC: Segmentations: zero all pixels < 150')
confusion_matrix(subject_level, 'filter_150')
```

```{r}
compute_aupr(subject_level$filter_150_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 150 and binarize')
compute_auroc(subject_level$filter_150_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 150 and binarize')
confusion_matrix(subject_level, 'filter_150_binarized')
```

```{r}
compute_aupr(subject_level$filter_150_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 150 and skeletonize')
compute_auroc(subject_level$filter_150_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 150 and skeletonize')
confusion_matrix(subject_level, 'filter_150_skeletonized')
```



### Segmentations: zero all pixels < 200

```{r}
compute_aupr(subject_level$filter_200, subject_level$label, 'PR: Segmentations: zero all pixels < 200')
compute_auroc(subject_level$filter_200, subject_level$label, 'ROC: Segmentations: zero all pixels < 200')
confusion_matrix(subject_level, 'filter_200')
```

```{r}
compute_aupr(subject_level$filter_200_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 200 and binarize')
compute_auroc(subject_level$filter_200_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 200 and binarize')
confusion_matrix(subject_level, 'filter_200_binarized')
```

```{r}
compute_aupr(subject_level$filter_200_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 200 and skeletonize')
compute_auroc(subject_level$filter_200_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 200 and skeletonize')
confusion_matrix(subject_level, 'filter_200_skeletonized')
```



### Segmentations: zero all pixels < 210

```{r}
compute_aupr(subject_level$filter_210, subject_level$label, 'PR: Segmentations: zero all pixels < 210')
compute_auroc(subject_level$filter_210, subject_level$label, 'ROC: Segmentations: zero all pixels < 210')
confusion_matrix(subject_level, 'filter_210')
```

```{r}
compute_aupr(subject_level$filter_210_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 210 and binarize')
compute_auroc(subject_level$filter_210_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 210 and binarize')
confusion_matrix(subject_level, 'filter_210_binarized')
```

```{r}
compute_aupr(subject_level$filter_210_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 210 and skeletonize')
compute_auroc(subject_level$filter_210_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 210 and skeletonize')
confusion_matrix(subject_level, 'filter_210_skeletonized')
```



### Segmentations: zero all pixels < 220

```{r}
compute_aupr(subject_level$filter_220, subject_level$label, 'PR: Segmentations: zero all pixels < 220')
compute_auroc(subject_level$filter_220, subject_level$label, 'ROC: Segmentations: zero all pixels < 220')
confusion_matrix(subject_level, 'filter_220')
```

```{r}
compute_aupr(subject_level$filter_220_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 220 and binarize')
compute_auroc(subject_level$filter_220_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 220 and binarize')
confusion_matrix(subject_level, 'filter_220_binarized')
```

```{r}
compute_aupr(subject_level$filter_220_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 220 and skeletonize')
compute_auroc(subject_level$filter_220_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 220 and skeletonize')
confusion_matrix(subject_level, 'filter_220_skeletonized')
```



### Segmentations: zero all pixels < 230

```{r}
compute_aupr(subject_level$filter_230, subject_level$label, 'PR: Segmentations: zero all pixels < 230')
compute_auroc(subject_level$filter_230, subject_level$label, 'ROC: Segmentations: zero all pixels < 230')
confusion_matrix(subject_level, 'filter_230')
```

```{r}
compute_aupr(subject_level$filter_230_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 230 and binarize')
compute_auroc(subject_level$filter_230_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 230 and binarize')
confusion_matrix(subject_level, 'filter_230_binarized')
```

```{r}
compute_aupr(subject_level$filter_230_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 230 and skeletonize')
compute_auroc(subject_level$filter_230_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 230 and skeletonize')
confusion_matrix(subject_level, 'filter_230_skeletonized')
```



### Segmentations: zero all pixels < 240

```{r}
compute_aupr(subject_level$filter_240, subject_level$label, 'PR: Segmentations: zero all pixels < 240')
compute_auroc(subject_level$filter_240, subject_level$label, 'ROC: Segmentations: zero all pixels < 240')
confusion_matrix(subject_level, 'filter_240')
```

```{r}
compute_aupr(subject_level$filter_240_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 240 and binarize')
compute_auroc(subject_level$filter_240_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 240 and binarize')
confusion_matrix(subject_level, 'filter_240_binarized')
```

```{r}
compute_aupr(subject_level$filter_240_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 240 and skeletonize')
compute_auroc(subject_level$filter_240_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 240 and skeletonize')
confusion_matrix(subject_level, 'filter_240_skeletonized')
```



### Segmentations: zero all pixels < 250

```{r}
compute_aupr(subject_level$filter_250, subject_level$label, 'PR: Segmentations: zero all pixels < 250')
compute_auroc(subject_level$filter_250, subject_level$label, 'ROC: Segmentations: zero all pixels < 250')
confusion_matrix(subject_level, 'filter_250')
```

```{r}
compute_aupr(subject_level$filter_250_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 250 and binarize')
compute_auroc(subject_level$filter_250_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 250 and binarize')
confusion_matrix(subject_level, 'filter_250_binarized')
```

```{r}
compute_aupr(subject_level$filter_250_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 250 and skeletonize')
compute_auroc(subject_level$filter_250_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 250 and skeletonize')
confusion_matrix(subject_level, 'filter_250_skeletonized')
```



### Segmentations: zero all pixels < 257

```{r}
compute_aupr(subject_level$filter_257, subject_level$label, 'PR: Segmentations: zero all pixels < 257')
compute_auroc(subject_level$filter_257, subject_level$label, 'ROC: Segmentations: zero all pixels < 257')
confusion_matrix(subject_level, 'filter_257')
```

```{r}
compute_aupr(subject_level$filter_257_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 257 and binarize')
compute_auroc(subject_level$filter_257_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 257 and binarize')
confusion_matrix(subject_level, 'filter_257_binarized')
```

```{r}
compute_aupr(subject_level$filter_257_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 257 and skeletonize')
compute_auroc(subject_level$filter_257_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 257 and skeletonize')
confusion_matrix(subject_level, 'filter_257_skeletonized')
```



### Segmentations: zero all pixels > 10

```{r}
compute_aupr(subject_level$filter_10, subject_level$label, 'PR: Segmentations: zero all pixels < 10')
compute_auroc(subject_level$filter_10, subject_level$label, 'ROC: Segmentations: zero all pixels < 10')
confusion_matrix(subject_level, 'filter_10')
```

```{r}
compute_aupr(subject_level$filter_10_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 10 and binarize')
compute_auroc(subject_level$filter_10_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 10 and binarize')
confusion_matrix(subject_level, 'filter_10_binarized')
```

```{r}
compute_aupr(subject_level$filter_10_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 10 and skeletonize')
compute_auroc(subject_level$filter_10_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 10 and skeletonize')
confusion_matrix(subject_level, 'filter_10_skeletonized')
```



### Segmentations: zero all pixels < 75 and > 150

```{r}
compute_aupr(subject_level$filter_75, subject_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150')
compute_auroc(subject_level$filter_75, subject_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150')
confusion_matrix(subject_level, 'filter_75')
```

```{r}
compute_aupr(subject_level$filter_75_binarized, subject_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and binarize')
compute_auroc(subject_level$filter_75_binarized, subject_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and binarize')
confusion_matrix(subject_level, 'filter_75_binarized')
```

```{r}
compute_aupr(subject_level$filter_75_skeletonized, subject_level$label, 'PR: Segmentations: zero all pixels < 75 and > 150 and skeletonize')
compute_auroc(subject_level$filter_75_skeletonized, subject_level$label, 'ROC: Segmentations: zero all pixels < 75 and > 150 and skeletonize')
confusion_matrix(subject_level, 'filter_75_skeletonized')
```



```{r}
aupr_figures <- function(predictions, labels, title, names, save_name) {
    n_black <- length(labels[labels == 1])
    n_total <- length(labels)
    for (i in seq_along(predictions)) {
        preds <- prediction(predictions[i], labels)
        aupr <- performance(preds, measure = 'aucpr')
        aupr <- aupr@y.values[[1]]
        perf <- performance(preds, 'prec', 'rec')
        if (is.null(dev.list())) {
            png(save_name,
                width=7,
                height=5,
                units='in',
                res=300)
            plot(perf,
                 main = title,
                 xlim=c(0,1),
                 ylim=c(0,1),
                 col = i,
                 lwd = 2)
        } else {
            plot(perf,
                 main = title,
                 xlim=c(0,1),
                 ylim=c(0,1),
                 col = i,
                 lwd = 2,
                 add = TRUE)
        }
    }
    abline(a = n_black/n_total,
           b = 0,
           col = 'red',
           lty = 2,
           lwd = 2)
    legend('bottomleft',
           names,
           lwd = 2,
           col = seq_along(predictions),
           bty = 'n',
           inset = c(0.1, 0.07))
    legend('bottomleft',
           paste('Null AUPR:', sprintf('%.3f', round(n_black / n_total, 3))),
           lwd = 2,
           lty = 2,
           col = 'red',
           bty = 'n',
           inset = c(0.1, 0.0))
}
```


## Figures for Paper
```{r}
aupr_figures(list(image_level$filter_0, image_level$filter_50, image_level$filter_200, image_level$filter_240),
             image_level$label,
             'Precision-Recall Curves of Thresholded RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/thresholded_image.png')
```

```{r}
aupr_figures(list(image_level$filter_0_binarized, image_level$filter_50_binarized, image_level$filter_200_binarized, image_level$filter_240_binarized),
             image_level$label,
             'Precision-Recall Curves of Binarized RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/binarized_image.png')
```

```{r}
aupr_figures(list(image_level$filter_0_skeletonized, image_level$filter_50_skeletonized, image_level$filter_200_skeletonized, image_level$filter_240_skeletonized),
             image_level$label,
             'Precision-Recall Curves of Skeletonized RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/skeletonized_image.png')
```

```{r}
aupr_figures(list(subject_level$filter_0, subject_level$filter_50, subject_level$filter_200, subject_level$filter_240),
             subject_level$label,
             'Precision-Recall Curves of Thresholded RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/thresholded_subject.png')
```

```{r}
aupr_figures(list(subject_level$filter_0_binarized, subject_level$filter_50_binarized, subject_level$filter_200_binarized, subject_level$filter_240_binarized),
             subject_level$label,
             'Precision-Recall Curves of Binarized RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/binarized_subject.png')
```

```{r}
aupr_figures(list(subject_level$filter_0_skeletonized, subject_level$filter_50_skeletonized, subject_level$filter_200_skeletonized, subject_level$filter_240_skeletonized),
             subject_level$label,
             'Precision-Recall Curves of Skeletonized RVMs',
             c('Threshold 0 AUPR:', 'Threshold 50 AUPR: ', 'Threshold 150 AUPR: ', 'Threshold 220 AUPR: '),
             './out/figures/skeletonized_subject.png')
```



## Evaluate on Manual Segmentations
```{r}
manual_binary_0 <- read_csv('./out/probabilities/manual_segmentations_binarized_0.csv') %>%
    mutate(img_name = basename(img_loc)) %>%
    separate(img_name, c('site', 'subject_id')) %>%
    filter(!is.na(as.numeric(subject_id))) %>%
    mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
    select(subject_id, probability) %>%
    inner_join(irop_data) %>%
    filter(race == 'African American' | race == 'Caucasian/White') %>%
    mutate(race = if_else(race == 'African American', 1, 0),
           across(race, as.factor))

manual_skeleton_0 <- read_csv('./out/probabilities/manual_segmentations_skeletonized_0.csv') %>%
    mutate(img_name = basename(img_loc)) %>%
    separate(img_name, c('site', 'subject_id')) %>%
    filter(!is.na(as.numeric(subject_id))) %>%
    mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
    select(subject_id, probability) %>%
    inner_join(irop_data) %>%
    filter(race == 'African American' | race == 'Caucasian/White') %>%
    mutate(race = if_else(race == 'African American', 1, 0),
           across(race, as.factor))

manual_binary_50 <- read_csv('./out/probabilities/manual_segmentations_binarized_50.csv') %>%
    mutate(img_name = basename(img_loc)) %>%
    separate(img_name, c('site', 'subject_id')) %>%
    filter(!is.na(as.numeric(subject_id))) %>%
    mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
    select(subject_id, probability) %>%
    inner_join(irop_data) %>%
    filter(race == 'African American' | race == 'Caucasian/White') %>%
    mutate(race = if_else(race == 'African American', 1, 0),
           across(race, as.factor))

manual_skeleton_50 <- read_csv('./out/probabilities/manual_segmentations_skeletonized_50.csv') %>%
    mutate(img_name = basename(img_loc)) %>%
    separate(img_name, c('site', 'subject_id')) %>%
    filter(!is.na(as.numeric(subject_id))) %>%
    mutate(subject_id = paste(toupper(site), subject_id, sep = '-')) %>%
    select(subject_id, probability) %>%
    inner_join(irop_data) %>%
    filter(race == 'African American' | race == 'Caucasian/White') %>%
    mutate(race = if_else(race == 'African American', 1, 0),
           across(race, as.factor))
```


```{r}
pred <- prediction(manual_binary_0$probability, manual_binary_0$race)

num_yes <- length(manual_binary_0$race[manual_binary_0$race == 1])
num_no <- length(manual_binary_0$race[manual_binary_0$race == 0])

perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Binarized - 0 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))
```


```{r}
pred <- prediction(manual_skeleton_0$probability, manual_skeleton_0$race)

num_yes <- length(manual_skeleton_0$race[manual_skeleton_0$race == 1])
num_no <- length(manual_skeleton_0$race[manual_skeleton_0$race == 0])

perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Skeletonized - 0 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))
```


```{r}
pred <- prediction(manual_binary_50$probability, manual_binary_50$race)

num_yes <- length(manual_binary_50$race[manual_binary_50$race == 1])
num_no <- length(manual_binary_50$race[manual_binary_50$race == 0])

perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Binarized - 50 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))
```


```{r}
pred <- prediction(manual_skeleton_50$probability, manual_skeleton_50$race)

num_yes <- length(manual_skeleton_50$race[manual_skeleton_50$race == 1])
num_no <- length(manual_skeleton_50$race[manual_skeleton_50$race == 0])

perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Skeletonized - 50 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))
```


```{r}
image_level <- read_csv('./out/probabilities/retcam_filtered_0.csv', col_types = cols()) %>%
    mutate(label = as.factor(if_else(str_detect(img_loc, 'black'), 1, 0)),
           image_id = as.numeric(file_path_sans_ext(basename(img_loc)))) %>%
    select(img_loc, image_id, label, retcam = probability) %>%
    bind_cols(select(read_csv('./out/probabilities/segmentations_binarized_test.csv', col_types = cols()), filter_0_binarized = probability))
```

```{r}
pred <- prediction(image_level$filter_0_binarized, image_level$label)

num_yes <- length(image_level$label[image_level$label == 1])
num_no <- length(image_level$label[image_level$label == 0])

perf <- performance(pred,'prec', 'rec')
plot(perf, colorize=TRUE, main = 'Manual Segmentations: Skeletonized - 0 Threshold Model', ylim = c(0,1))
abline(a = num_yes / num_no, b = 0, col = 'red', lty = 2)
aupr <- performance(pred, measure = 'aucpr')
aupr <- aupr@y.values[[1]]
text(0.8, 0.9, paste('AUPR: ', sprintf('%.3f', round(aupr, 3)), sep = ''))
```